Processing condition setting support system

Abstract

To provide a machining condition setting support system for enabling more appropriate machining conditions to be determined by enabling stability limit diagrams to be represented more highly accurately.SOLUTION: A machining condition setting support system 1 comprises: a stability limit diagram generation unit 13 capable of generating stability limit diagrams SD1 to SD4, corresponding respectively to cutting depth sections Aa1 to Aa4 which are a plurality of sections into which a cutting depth that can be determined to be included in machining conditions is divided, the stability limit diagrams SD1 to SD4 each defining a relation between a rotation speed of a tool T or a workpiece W, and a maximum cutting depth at which chatter does not occur; and a chatter occurrence determination unit 14 configured to determine, by using stability limit diagrams SD1 to SD4 corresponding to an evaluation object cutting depth as the machining conditions, out of the plurality of stability limit diagrams SD1 to SD4 that may be generated, whether the chatter occurs in the case with the evaluation object cutting depth and an evaluation object rotation speed included in the machining conditions.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a processing condition setting support system. [Background technology]

[0002] Patent Documents 1 and 2 describe determining machining conditions using a stability limit diagram, which defines the relationship between the rotational speed of the tool or workpiece and the maximum depth of cut at which chatter does not occur, in order to suppress chatter (also called chatter vibration) during cutting. In Patent Documents 1 and 2, the stability limit diagram is generated using dynamic characteristics represented by the mass (M), damping coefficient (C), and rigidity (K) of the tool or workpiece, as well as specific cutting resistance. Specific cutting resistance is the value obtained by dividing the cutting resistance by the cutting cross-sectional area, i.e., the cutting resistance per unit cross-sectional area.

[0003] It is also known that in order to determine the processing conditions for a processing device, the knowledge of experts is compiled into a database and the database is utilized. For example, Patent Document 3 describes a knowledge model compiled into a database. The knowledge model is a model that is configured from multiple factors defined by various technical terms and the relationships between the factors. It is said that the knowledge model can be used to optimize the processing conditions.

[0004] Patent Document 4 describes the application of machine learning when defining the relationships between factors in a knowledge model. In particular, as means for defining the relationships between factors, it describes a means for defining the correspondence relationship (rank-rank relationship) between the rank value of a reference factor and the rank value of a factor to be connected, and a means for defining the correspondence relationship (rank-range relationship) between the rank value of a reference factor and the range value of a factor to be connected. Furthermore, when a reference factor has a relationship with multiple factors, the influence of multiple factors on the reference factor can be determined by setting contribution degrees. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent No. 5862111 [Patent Document 2] Japanese Patent Application Laid-Open No. 2012-200844 [Patent Document 3] Japanese Patent Publication No. 2020-177547 [Patent Document 4] Patent Publication No. 2021-071856 Summary of the Invention [Problem to be solved by the invention]

[0006] A stability limit diagram shows the relationship between the rotational speed of a tool or workpiece and the maximum depth of cut at which chatter does not occur. Machining conditions are determined using a stability limit diagram, for example, as follows. When determining machining conditions to improve cutting efficiency, one method is to increase the depth of cut. In this case, a rotational speed at which the maximum depth of cut on the stability limit diagram shows a large value is selected, and a depth of cut smaller than the maximum depth of cut corresponding to that rotational speed on the stability limit diagram is set. In this way, the determined rotational speed and depth of cut as machining conditions result in machining in a stable region on the stability limit diagram, making it possible to suppress chatter.

[0007] However, while the stability limit diagram shows the relationship between the rotational speed and the maximum depth of cut at which chatter does not occur, it is known that the value of the maximum depth of cut itself contains variations. Therefore, when determining the depth of cut as a machining condition, it has been common to set the depth of cut to a value that is sufficiently smaller than the maximum depth of cut in the stability limit diagram. In other words, while the stability limit diagram can be used qualitatively when determining the depth of cut, it has been difficult to use it quantitatively. As such, there is room for improvement in the chatter occurrence determination using the stability limit diagram that has been generated up to now.

[0008] The present invention has been made in consideration of the above-mentioned problems, and aims to provide a machining condition setting support system that enables more accurate representation of the stability limit diagram, thereby enabling more appropriate machining conditions to be determined. [Means for solving the problem]

[0009] One aspect of the present invention is a machining condition setting support system that includes a processor and evaluates or sets machining conditions including at least a cutting amount and a rotation speed when performing cutting while rotating at least one of a tool and a workpiece, The processor: a stability limit diagram generating unit configured to be able to generate stability limit diagrams in which the depth of cut that can be taken as the machining condition is divided into a plurality of sections, the stability limit diagrams corresponding to the divided depth of cut sections each defining a relationship between a rotation speed of the tool or the workpiece and a maximum depth of cut that does not cause chatter; a chatter occurrence determination unit configured to determine whether chatter occurs in the case of an evaluation target cutting-in amount and an evaluation target rotation speed included in the machining conditions, using a target stability limit diagram corresponding to an evaluation target cutting-in amount among the plurality of stability limit diagrams that can be generated; The present invention relates to a processing condition setting support system comprising: [Effects of the Invention]

[0010] According to the above aspect, the stability limit diagram generating unit can generate a stability limit diagram corresponding to each of the cutting-in amount sections when the cutting-in amount that can be taken as a machining condition is divided into a plurality of sections. In other words, there is not one type of stability limit diagram, but multiple types corresponding to each cutting-in amount section.

[0011] By discovering that the stability limit diagram changes depending on the cutting depth and finding the relationship between the cutting depth and the stability limit diagram, it is possible to generate a stability limit diagram corresponding to each divided cutting depth interval, as described above. Therefore, the stability limit diagram can be displayed with higher accuracy than before.

[0012] The chatter occurrence determination unit is configured to determine whether chatter occurs for the cutting depth to be evaluated and the rotational speed to be evaluated as machining conditions. This determination uses a target stability limit diagram corresponding to the cutting depth to be evaluated. As described above, there are multiple types of stability limit diagrams corresponding to each of the divided cutting depth sections. Therefore, for the determination, a stability limit diagram corresponding to the cutting depth section including the cutting depth to be evaluated is used. Then, using this stability limit diagram, it is determined whether chatter occurs for the cutting depth to be evaluated and the rotational speed to be evaluated. In this way, chatter determination is performed using a stability limit diagram corresponding to the cutting depth to be evaluated. This makes it possible to determine more appropriate machining conditions.

[0013] As described above, according to the above aspect, it is possible to provide a machining condition setting support system that enables the stability limit diagram to be represented with higher accuracy, thereby enabling more appropriate machining conditions to be determined. [Brief explanation of the drawings]

[0014] [Figure 1] FIG. 1 is a diagram showing the configuration of a processing condition setting support system according to a first embodiment. [Figure 2] FIG. 10 is a diagram showing a cutting model. [Figure 3] 2 is a diagram showing the configuration of a management device that constitutes the machining condition setting support system of FIG. 1. FIG. [Figure 4] 4 is a diagram showing a stability limit diagram generated by a stability limit diagram generating unit configuring the management device of FIG. 3. FIG. [Figure 5] 4 is a diagram showing the configuration of a specific cutting resistance generating unit that constitutes the management device of FIG. 3. FIG. [Figure 6]6 is a diagram showing the configuration of a knowledge model storage unit that constitutes the specific cutting resistance generating unit of FIG. 5. FIG. [Figure 7] FIG. 7 is a diagram showing the concept of a conditional probability table which is a part of the knowledge model storage unit in FIG. 6. [Figure 8] 6 is a diagram showing the configuration of a knowledge model generation unit that constitutes the specific cutting resistance generation unit of FIG. 5. FIG. [Figure 9] 9 is a conditional probability table generated based on the parameters of the prior distribution stored in a prior distribution storage unit that constitutes the knowledge model generation unit of FIG. 8. [Figure 10] 9 is a conditional probability table generated based on a posterior distribution generated by a conditional probability table generation unit constituting the knowledge model generation unit of FIG. 8. [Figure 11] 1 shows the procedure for updating a conditional probability table, in which the initial state of the conditional probability table is generated using only the parameters of the prior distribution. [Figure 12] 10 shows a procedure for updating a conditional probability table, in which an updated conditional probability table is generated based on a posterior distribution. [Figure 13] This shows the procedure for updating the conditional probability table, in which a further updated conditional probability table is generated based on a new posterior distribution. [Figure 14] 6 is a flowchart showing a process when forward probabilistic inference is performed by a specific cutting resistance output unit that constitutes the specific cutting resistance generating unit of FIG. 5. [Figure 15] 6 is a flowchart showing a process when a specific cutting resistance output unit constituting the specific cutting resistance generating unit of FIG. 5 performs backward probabilistic inference. [Figure 16] FIG. 10 is a diagram showing the configuration of a processing condition setting support system according to a second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0015] (Embodiment 1) 1.Configuration of processing condition setting support system 1 The configuration of a machining condition setting support system 1 according to the first embodiment will be described with reference to Fig. 1. The machining condition setting support system 1 evaluates or sets machining conditions such as the cutting depth and rotation speed using a stability limit diagram related to the cutting process when cutting a workpiece W with a tool T while rotating at least one of the tool T and the workpiece W.

[0016] In particular, in this embodiment, the machining condition setting support system 1 generates a stability limit diagram using the specific cutting resistance in cutting. Furthermore, the machining condition setting support system 1 utilizes a knowledge model M for estimating the specific cutting resistance in cutting. That is, the machining condition setting support system 1 estimates the specific cutting resistance by using the knowledge model M based on the machining conditions of cutting, the specifications of the tool T, and the specifications of the workpiece W.

[0017] As shown in Fig. 1, the machining condition setting support system 1 is configured to include a management device 2 that is mainly operated by an administrator, a display device 3 that is held by the administrator and workers, and multiple machining devices 4 and 5 that perform cutting. The management device 2, the display device 3, and the machining devices 4 and 5 form a network and are configured to be able to communicate with each other. In other words, the management device 2, the display device 3, and the machining devices 4 and 5 are devices that form the network.

[0018] The management device 2 is configured with a processor 2a, a storage device 2b, a display device 2c, and an input device 2d. The processor 2a performs arithmetic processing such as generating a stability limit diagram and determining chatter, while the storage device 2b stores a mechanical compliance transfer function (hereinafter referred to as the "compliance transfer function") and a knowledge model M. The management device 2 can also function as a server that manages processing devices 4 and 5. The processing devices 4 and 5 are, for example, lathes, machining centers, milling machines, gear cutting machines, boring machines, etc.

[0019] 2. Machining model The cutting model will be described with reference to Fig. 2. Fig. 2 shows a model using turning as an example, in which a workpiece W rotates around a central axis L and a tool T is fed in the axial direction of the workpiece W. However, the cutting model shown in Fig. 2 is a model that can be applied to cutting in general, and is also a model that can be applied in the same way when cutting is performed by rotating the tool T.

[0020] As shown in Figure 2, when a workpiece W is cut by a tool T, cutting resistance F occurs at a cutting point P. The cutting resistance F is represented by a principal force F1, a thrust force F2, and a feed force F3. In Figure 2, the principal force F1 corresponds to the component in the tangential direction of a circle centered on the central axis L of the workpiece W, the thrust force F2 corresponds to the component in the normal direction to the cutting point P, and the feed force F3 corresponds to the component in the feed direction of the tool T (the axial direction of the workpiece W). The principal force F1, thrust force F2, and feed force F3 are mutually orthogonal. Note that the cutting resistance F can also be decomposed into the principal force F1, thrust force F2, and feed force F3 in cutting processes other than those using a lathe.

[0021] 3. Configuration of management device 2 The configuration of the management device 2 will be described with reference to Fig. 3. As shown in Fig. 3, the management device 2 includes, as a processor 2a and a storage device 2b, at least a compliance transfer function storage unit 11, a specific cutting resistance generation unit 12, a stability limit diagram generation unit 13, a chatter occurrence determination unit 14, a recommended condition determination unit 15, and a teaching unit 16. Furthermore, as shown in Fig. 3, the management device 2 includes a display device 2c in addition to the above units 11 to 16.

[0022] The compliance transfer function storage unit 11 stores the compliance transfer function of at least one of the tool T and the workpiece W. The compliance transfer function corresponds to the dynamic characteristics of at least one of the tool T and the workpiece W. In other words, the compliance transfer function corresponds to the dynamic characteristics represented by the mass (M), damping coefficient (C), and stiffness (K) of the tool T or the workpiece W.

[0023] More specifically, the compliance transfer function is a transfer function of the response displacement of the tool T when an excitation force is applied to the tool T, a transfer function of the response displacement of the workpiece W when an excitation force is applied to the workpiece W, and a transfer function relating to both the tool T and the workpiece W. The compliance transfer function is a function relating to the response displacement of at least one of the tool T and the workpiece W with respect to the rotation frequency of at least one of the tool T and the workpiece W. The compliance transfer function can be obtained, for example, by a hammering test, simulation, theoretical calculation, etc. Note that the method of generating the compliance transfer function is well known, and therefore a detailed description thereof will be omitted. The compliance transfer function storage unit 11 is configured by the storage device 2b.

[0024] The specific cutting resistance generator 12 generates the specific cutting resistance based on the machining conditions, the specifications of the tool T, and the specifications of the workpiece W. The specific cutting resistance corresponds to the value obtained by dividing the cutting resistance F by the cutting cross-sectional area. However, it has been found that the thrust force F2 of the cutting resistance F has a large effect on chatter. Therefore, in this embodiment, the specific cutting resistance is set to the value obtained by dividing the thrust force F2 (shown in FIG. 2) that constitutes the cutting resistance F by the cutting cross-sectional area.

[0025] As will be described later, the specific cutting resistance generating unit 12 is configured to generate an estimated value of the specific cutting resistance by performing forward probabilistic inference using the conditional probability table M1 that constitutes the knowledge model M. The specific cutting resistance generating unit 12 can also determine the machining conditions of the cutting work, the specifications of the tool T, and the specifications of the workpiece W by performing backward probabilistic inference. The specific cutting resistance generating unit 12 is configured by a processor 2a and a storage device 2b.

[0026] The stability limit diagram generating unit 13 is configured to generate a stability limit diagram for cutting. The stability limit diagram is a diagram that defines the relationship between the rotational speed of the tool T or workpiece W and the maximum cutting depth at which chatter does not occur. The lower region of the stability limit diagram corresponds to a stable region where chatter does not occur, and the upper region corresponds to an unstable region where chatter may occur. The stability limit diagram generating unit 13 is configured by the processor 2a.

[0027] When generating the stability limit diagram, the stability limit diagram generating unit 13 uses the compliance transfer function stored in the compliance transfer function storage unit 11 and the estimated value of the specific cutting resistance generated by the specific cutting resistance generating unit 12. The specific cutting resistance used here is the specific cutting resistance corresponding to the depth of cut to be evaluated as a machining condition.

[0028] In this embodiment, the stability limit diagram generating unit 13 is configured to divide the depth of cut that can be taken as a machining condition into a plurality of sections, and to be able to generate stability limit diagrams SD1, SD2, SD3, and SD4 corresponding to each of the divided depth of cut sections. In other words, the stability limit diagram generating unit 13 is configured to be able to generate a plurality of types of stability limit diagrams SD1, SD2, SD3, and SD4, rather than generating one type of stability limit diagram. Then, the stability limit diagram generating unit 13 generates one target stability limit diagram corresponding to the depth of cut that is to be evaluated as a machining condition, from the plurality of types of stability limit diagrams SD1, SD2, SD3, and SD4 that can be generated.

[0029] For example, assume that the cutting depth available as a machining condition is 0.30 mm to 0.9 mm. In this case, the divided cutting depth intervals are, for example, 0.30 to 0.50 mm, 0.50 to 0.67 mm, 0.67 to 0.80 mm, 0.80 to 0.90 mm, etc. Note that the cutting depth intervals are irregularly spaced, but they may also be equally spaced. The available cutting depths and the numerical values ​​of the cutting depth intervals are merely examples and may be changed as appropriate depending on the machining process, such as rough machining, semi-finishing machining, or finishing machining, or the type of machining, such as turning, drilling, milling, or gear cutting. Details of the multiple types of stability limit diagrams SD1, SD2, SD3, and SD4 will be described later.

[0030] The chatter occurrence determination unit 14 is configured to determine whether chatter will occur for the cut-in amount to be evaluated and the rotational speed to be evaluated included in the machining conditions, using a target stability limit diagram (one of SD1, SD2, SD3, SD4) that corresponds to the cut-in amount to be evaluated as a machining condition, out of multiple stability limit diagrams SD1, SD2, SD3, SD4 that can be generated by the stability limit diagram generation unit 13. The chatter occurrence determination unit 14 is configured by the processor 2a.

[0031] The recommended condition determination unit 15 determines recommended values ​​of machining conditions using the stability limit diagrams SD1, SD2, SD3, and SD4 generated by the stability limit diagram generation unit 13. At this time, the recommended condition determination unit 15 can determine the recommended values ​​of machining conditions taking into consideration the judgment results by the chatter occurrence judgment unit 14. In detail, the recommended condition determination unit 15 sets recommended values ​​as machining conditions that are located in the lower region of the stability limit diagrams SD1, SD2, SD3, and SD4. The recommended condition determination unit 15 is configured by the processor 2a.

[0032] The teaching unit 16 teaches the recommended values ​​of the machining conditions determined by the recommended condition determination unit 15. As a means for teaching the recommended values ​​of the machining conditions, the teaching unit 16 displays them on the display device 2c constituting the management device 2, or on the display device 3. The display device 3 displays the recommended values ​​of the machining conditions acquired from the teaching unit 16 via communication.

[0033] Furthermore, the teaching unit 16 transmits the recommended values ​​of the machining conditions to the machining devices 4 and 5 via communication. In this case, the machining devices 4 and 5 can acquire the recommended values ​​of the machining conditions taught by the teaching unit 16 and perform cutting processing using the acquired recommended values ​​of the machining conditions. In other words, the machining devices 4 and 5 autonomously change the machining conditions and perform cutting processing based on the changed machining conditions. The machining results when cutting processing is performed by the machining devices 4 and 5 are transmitted to the specific cutting resistance generation unit 12 as observation data. The specific cutting resistance generation unit 12 can generate and update the knowledge model M using the observation data in the machining devices 4 and 5. The teaching unit 16 is configured by the processor 2a.

[0034] 4. Details of the stability limit diagrams SD1 to SD4 for multiple species The plurality of types of stability limit diagrams SD1, SD2, SD3, and SD4 that can be generated by the stability limit diagram generating unit 13 will be described with reference to Fig. 4. In this embodiment, the cutting depth that can be obtained as a machining condition is assumed to be 0.30 mm to 0.9 mm. The cutting depth that can be obtained is divided into cutting depth intervals Aa1, Aa2, Aa3, and Aa4 that are assumed to be 0.30 to 0.50 mm, 0.50 to 0.67 mm, 0.67 to 0.80 mm, and 0.80 to 0.90 mm, respectively.

[0035] The stability limit diagram SD1 shown in Figure 4(a) corresponds to the cutting depth range Aa1, which is 0.30 to 0.50 mm. The stability limit diagram SD2 shown in Figure 4(b) corresponds to the cutting depth range Aa2, which is 0.50 to 0.67 mm. The stability limit diagram SD3 shown in Figure 4(c) corresponds to the cutting depth range Aa3, which is 0.67 to 0.80 mm. The stability limit diagram SD4 shown in Figure 4(d) corresponds to the cutting depth range Aa4, which is 0.80 to 0.90 mm.

[0036] Normally, each of the stability limit diagrams SD1 to SD4 would be generated to correspond to a specific cutting-in amount. However, in this embodiment, the stability limit diagrams SD1 to SD4 are generated to correspond to each of the divided cutting-in amount sections Aa1 to Aa4, rather than to a specific cutting-in amount. The reason for this is as follows.

[0037] As described above, the stability limit diagrams SD1 to SD4 are generated using the compliance transfer function and the estimated value of the specific cutting resistance. The estimated value of the specific cutting resistance is generated based on the cutting depth and is set to a value corresponding to one of the above-mentioned cutting depth intervals Aa1 to Aa4. Therefore, the cutting depth intervals Aa1 to Aa4, which are the basis for calculating the estimated value of the specific cutting resistance, correspond to the cutting depth intervals Aa1 to Aa4 on the stability limit diagrams SD1 to SD4. As shown in (a), (b), (c), and (d) of FIG. 4, each of the stability limit diagrams SD1 to SD4 corresponds to a respective cutting depth interval Aa1 to Aa4. Therefore, the stability limit diagrams SD1 to SD4 are generated to correspond to each of the divided cutting depth intervals Aa1 to Aa4.

[0038] Furthermore, in this embodiment, the cutting-in amount sections Aa1 to Aa4 are unequally spaced. The reason for this is as follows: As will be described later, it has been found that the change in specific cutting resistance increases as the cutting-in amount decreases and decreases as the cutting-in amount increases. Therefore, the cutting-in amount sections Aa1 to Aa4, which are the basis for calculating the estimated value of specific cutting resistance, are set wide in a range where the change in specific cutting resistance relative to the cutting-in amount is small, and are set narrow in a range where the change in specific cutting resistance relative to the cutting-in amount is large. In other words, the cutting-in amount sections Aa1 to Aa4 are set wider as the cutting-in amount decreases and narrower as the cutting-in amount increases.

[0039] 5. Configuration of specific cutting resistance generating unit 12 The configuration of the specific cutting resistance generating unit 12 will be described with reference to Figures 5 to 7. As shown in Figure 5, the specific cutting resistance generating unit 12 is configured with a knowledge model generating unit 21, a knowledge model storing unit 22, and a specific cutting resistance output unit 23. The knowledge model generating unit 21 is configured with a processor 2a and a storage device 2b, the specific cutting resistance output unit 23 is configured with the processor 2a, and the knowledge model storing unit 22 is configured with the storage device 2b.

[0040] The knowledge model generation unit 21 generates a knowledge model M. The knowledge model storage unit 22 stores the knowledge model M generated by the knowledge model generation unit 21.

[0041] As shown in Figure 6, the knowledge model M includes a conditional probability table M1 and probability tables M2, M3, and M4 for factors A, B, and C. The conditional probability table M1 is a model for estimating the specific cutting resistance. As mentioned above, chatter is significantly affected by the thrust force F2 of the cutting resistance F in Figure 2. Therefore, the specific cutting resistance to be estimated is the value obtained by dividing the thrust force F2 of the cutting resistance F by the cutting cross-sectional area.

[0042] The conditional probability table M1 defines the probability of the factor value of child factor D being true when the factor values ​​of at least parent factors A, B, and C are set as conditions. Here, the specific cutting resistance is affected by the cutting conditions, the specifications of tool T, the specifications of workpiece W, etc. In other words, the specific cutting resistance is a value that differs depending on the cutting conditions, the specifications of tool T, and the specifications of workpiece W.

[0043] Therefore, in the conditional probability table M1, the parent factors A, B, and C are factors related to the cutting conditions, factors related to the specifications of the tool T, and factors related to the specifications of the workpiece W. The conditional probability table M1 may further include factors other than the above factors A, B, and C as parent factors. The child factor D is the specific cutting resistance.

[0044] That is, the conditional probability table M1, which constitutes one of the knowledge models M, has at least the cutting conditions, the specifications of the tool T, and the specifications of the workpiece W as parent factors A, B, and C, and the specific cutting resistance as child factor D. The conditional probability table M1 is a model for estimating the specific cutting resistance based on the cutting conditions, the specifications of the tool T, and the specifications of the workpiece W.

[0045] As shown in Fig. 7, the machining conditions as parent factors A include, for example, the depth of cut, cutting speed, and feed rate. In Fig. 7, Aa is a symbol representing the depth of cut, and Ab is a symbol collectively representing the cutting speed and feed rate. The specifications of tool T as parent factors B include, for example, the material (material quality) of tool T, the shape of tool T (cutting edge angle, etc.), whether or not coating is performed, and the type of coating. The specifications of workpiece W as parent factors C include, for example, the material (material quality) of workpiece W, the shape of workpiece W, whether or not heat treatment is performed, and the type of heat treatment.

[0046] Furthermore, the machining conditions as parent factors A, B, and C, the specifications of tool T, and the specifications of workpiece W are each expressed by a numerical factor value. In the case of machining conditions as parent factor A, the numerical values ​​representing the cutting depth (Aa), cutting speed (Ab), feed rate (Ab), etc. are each the factor values ​​of parent factor A. In the case of the specifications of tool T as parent factor B, the physical property values ​​representing the material of tool T, the cutting edge angle representing the shape of tool T, etc., and the physical property values ​​representing the type of coating are each the factor values ​​of parent factor B. In the case of the specifications of workpiece W as parent factor C, the same is true for the specifications of tool T.

[0047] The conditional probability table M1 is conceptually represented as Table 1.

[0048] [Table 1]

[0049] For example, the conditional probability table M1 defines probabilities P1(1) to P1(8), P1(11) to P1(18), P2(1) to P2(8), P2(11) to P2(18), P3(1) to P3(8), and P3(11) to P3(18) that correspond to factor values ​​D1, D2, and D3 of specific cutting resistance as child factor D, when conditions are set as cut-in depth intervals Aa1 and Aa2 as the factor value of cut-in depth Aa of parent factor A, factor values ​​Ab1 and Ab2 representing feed rate and cutting speed of parent factor A, factor values ​​B1 and B2 of parent factor B, and factor values ​​C1 and C2 of parent factor C. In the conditional probability table M1 shown in Table 1, the sum of the probabilities in the vertical columns is 1. For example, the sum of the probabilities P1(1), P2(1), and P3(1) is 1.

[0050] In Table 1, the cutting-depth intervals Aa1 and Aa2, which are the factor values ​​of the cutting-depth Aa of parent factor A, correspond to the cutting-depth intervals Aa1 and Aa2 into which the cutting-depths that can be taken as machining conditions are divided in the stability limit diagram generating unit 13. Furthermore, the factor values ​​Ab1-Ab2, B1-B2, C1-C2, and D1-D3 of the other factors Ab, C, and D may each be a single value or a range of values. For example, the factor values ​​Ab1 and Ab2 of parent factor Ab may be, for example, a cutting speed value, the factor values ​​B1 and B2 of parent factor B may be, for example, a cutting angle value of tool T, and the factor values ​​C1 and C2 of parent factor C may be, for example, a physical property value representing the material of workpiece W.

[0051] For example, if the factor values ​​of parent factors Aa, Ab, B, and C are Aa1, Ab1, B1, and C1, the probability that the factor value D1 of the specific cutting resistance, which is child factor D, corresponds to is P1(1), the probability that the factor value D2 corresponds to is P2(1), and the probability that the factor value D3 corresponds to is P3(1). For example, P1(1) is 0.667, P2(1) is 0.333, and P3(1) is 0.

[0052] As described above, the knowledge model M includes the conditional probability table M1 shown in Table 1, as well as probability tables M2, M3, and M4 for factors A, B, and C shown in Tables 2 to 4. Table 2 is the probability table M2 for factor A, Table 3 is the probability table M3 for factor B, and Table 4 is the probability table M4 for factor C.

[0053] [Table 2]

[0054] [Table 3]

[0055] [Table 4]

[0056] For example, probability table M2 for factor A defines probabilities Pa1 and Pa2 corresponding to factor values ​​A1 and A2 of factor A as shown in Table 2. The sum of probabilities Pa1 and Pa2 is 1. Probability table M3 for factor B defines probabilities Pb1 and Pb2 corresponding to factor values ​​B1 and B2 of factor B as shown in Table 3. The sum of probabilities Pb1 and Pb2 is 1. Probability table M4 for factor C defines probabilities Pc1 and Pc2 corresponding to factor values ​​C1 and C2 of factor C as shown in Table 4. The sum of probabilities Pc1 and Pc2 is 1.

[0057] 5, the specific cutting resistance output unit 23 outputs an estimated value of the specific cutting resistance using the knowledge model M stored in the knowledge model storage unit 22. In detail, when the specific cutting resistance output unit 23 acquires the factor values ​​of the parent factors A, B, and C, it performs forward probabilistic inference using the conditional probability table M1 that constitutes the knowledge model M. The forward probabilistic inference is to estimate the specific cutting resistance, which is the child factor D, based on the parent factors A, B, and C that are defined as conditions.

[0058] In this way, the specific cutting resistance output unit 23 outputs an estimated value of the specific cutting resistance as the child factor D. For example, the specific cutting resistance output unit 23 can use the expected value as the output value. That is, the factor values ​​D1, D2, and D3 of the child factor D are multiplied by the corresponding probabilities P1(1), P2(1), and P3(1), respectively, and the sum of the obtained values ​​is output as the specific cutting resistance. In this case, the specific cutting resistance is "D1×P1(1)+D2×P2(1)+D3×P3(1)".

[0059] When the specific cutting resistance output unit 23 acquires the target factor value of the child factor D, which is the target value of the specific cutting resistance, it can also perform backward probabilistic inference using the conditional probability table M1 constituting the knowledge model M. The backward probabilistic inference is to estimate the parent factors A, B, and C defined as conditions based on the factor value of the child factor D. In this case, the specific cutting resistance output unit 23 can further use the probability tables M2, M3, and M4 of the factors A, B, and C to output factor values ​​for the cutting conditions, the specifications of the tool T, and the specifications of the workpiece W as parent factors.

[0060] 6. Configuration of knowledge model generation unit 21 The knowledge model generation unit 21 generates a knowledge model M as shown in FIG. 5. In particular, the knowledge model generation unit 21 generates a conditional probability table M1 that constitutes the knowledge model M. Here, the conditional probability table M1 may be set by directly inputting the values ​​of probabilities P1(1) to P1(8), P1(11) to P1(18), P2(1) to P2(8), P2(11) to P2(18), P3(1) to P3(8), and P3(11) to P3(18). Furthermore, the conditional probability table M1 may be generated using Bayesian estimation, as described below. Furthermore, the conditional probability table M1 may be updated using Bayesian estimation.

[0061] The configuration of the knowledge model generation unit 21 will be described with reference to Fig. 8 to Fig. 13. As shown in Fig. 8, the knowledge model generation unit 21 includes an observation data acquisition unit 41, an interval determination unit 42, an interval edit processing unit 43, a prior distribution storage unit 44, a conditional probability table generation unit 45, a weight storage unit 46, and a model edit processing unit 47. The observation data acquisition unit 41, the interval determination unit 42, the interval edit processing unit 43, the conditional probability table generation unit 45, and the model edit processing unit 47 are configured by the processor 2a, and the prior distribution storage unit 44 and the weight storage unit 46 are configured by the storage device 2b.

[0062] The observation data acquisition unit 41 acquires observation data 41a regarding the factor values ​​of parent factors A, B, and C and the factor value of child factor D. The observation data 41a may be data obtained by performing actual cutting work, or may be data obtained by performing a simulation of cutting work. The observation data 41a may also be data obtained by performing actual cutting work, or data obtained by performing a simulation of cutting work.

[0063] The observation data acquisition unit 41 is configured to be able to communicate with the processing devices 4 and 5, and is therefore able to acquire observation data 41a when actual cutting processing is performed by the processing devices 4 and 5. The observation data acquisition unit 41 can acquire the observation data 41a of the processing devices 4 and 5 in real time, or can acquire a collection of observation data 41a for a predetermined period of time rather than in real time. Furthermore, the observation data acquisition unit 41 can centrally acquire the observation data 41a of multiple processing devices 4 and 5.

[0064] The observation data acquisition unit 41 acquires the machining conditions used in the actual cutting, the specifications of the tool T, and the specifications of the workpiece W as data obtained by performing actual cutting. Furthermore, when actual cutting is performed, cutting resistance data is detected by a sensor such as a cutting dynamometer installed on the processing device 4, 5, and the cutting resistance data is divided by the cutting cross-sectional area to calculate the specific cutting resistance. Then, the observation data acquisition unit 41 acquires the calculated specific cutting resistance.

[0065] In this way, when actual cutting processing is performed, the observation data acquisition unit 41 can acquire observation data 41a about the factor values ​​of the parent factors A, B, and C and the factor value of the child factor D. Note that the sensors installed on the processing devices 4 and 5 can also calculate the specific cutting resistance by acquiring the power values ​​(current values, etc.) of each drive motor in addition to the cutting dynamometer.

[0066] Furthermore, when a cutting simulation is performed, the observation data acquisition unit 41 can acquire the machining conditions, the specifications of the tool T, and the specifications of the workpiece W as input information for the simulation, and can also acquire the specific cutting resistance as a result of the simulation. In this way, when a cutting simulation is performed, the observation data acquisition unit 41 can acquire observation data 41a regarding the factor values ​​of the parent factors A, B, and C and the factor value of the child factor D.

[0067] The interval determination unit 42 determines discrete intervals by discretizing the factor values ​​of parent factors A, B, and C and the factor value of child factor D in the conditional probability table M1 shown in Table 1. The interval determination unit 42 can select one of a plurality of discrete interval determination methods. For example, the interval determination unit 42 can determine intervals in which the factor values ​​of the factors A, B, C, and D are evenly discretized.

[0068] Furthermore, the interval determination unit 42 can determine intervals obtained by unevenly discretizing the factor values ​​of the factors A, B, C, and D. The cutting depth intervals Aa1, Aa2, Aa3, and Aa4 obtained by unevenly discretizing the factor value of the cutting depth as the parent factor Aa can be set to 0.30 to 0.50 mm, 0.50 to 0.67 mm, 0.67 to 0.80 mm, 0.80 to 0.90 mm, etc., as described with reference to FIG.

[0069] Furthermore, as an example of unequal discretization, the interval determination unit 42 can also determine discrete intervals based on the mutual information I(X;Y) between each random variable in each of the factors A, B, C, and D. The mutual information I(X;Y) is a quantity that represents a measure of the interdependence between two random variables. The mutual information I(X;Y) indicates how much one variable can be predicted by understanding the other variable. Furthermore, as another example of unequal discretization, the interval determination unit 42 can determine intervals that are discretized at equal frequency.

[0070] For example, the depth of cut interval can be determined based on the mutual information I(X;Y) between the depth of cut and the specific cutting resistance. The change in the specific cutting resistance increases as the depth of cut decreases and decreases as the depth of cut increases. Therefore, the depth of cut interval determined based on the mutual information I(X;Y) is set wider as the depth of cut decreases and narrower as the depth of cut increases.

[0071] The section edit processing unit 43 is configured to receive input from an operator and to be able to edit discrete sections in accordance with the operator's input. The section edit processing unit 43 makes it possible to adjust the discrete sections determined by the section determination unit 42 based on the operator's input. Note that the section edit processing unit 43 can also create discrete sections from scratch based on the operator's input.

[0072] The prior distribution storage unit 44 stores the parameter α of the preset prior distribution 44a to determine the conditional probability table M1. jkIn the following, for ease of explanation, the relationship between the parent factor A and the child factor D will be explained. The prior distribution 44a uses a Dirichlet distribution as expressed in equation (1). In equation (1), j is a discrete interval of the parent factor A, and k is a discrete interval of the child factor D.

[0073]

number

[0074] Furthermore, the prior distribution storage unit 44 stores the parameter α jk The expected value θa of the prior distribution 44a generated based on jk The conditional probability table 44b stored in the prior distribution storage unit 44 is generated based on the discrete intervals determined by the interval determination unit 42.

[0075] Expected value θa of prior distribution 44a jk is expressed by formula (2). In formula (2), Na j is the parameter α of the prior distribution 44a in the discrete interval j. jk is the sum of Na jk is the parameter α of the prior distribution 44a in the discrete interval j, k. jk is.

[0076]

number

[0077] The conditional probability table 44b shown in FIG. 9 shows the corresponding expected value θa jk Here, the parameter α of the prior distribution 44a is jk is set based on, for example, the worker's experience, information on similar fields, etc. In this case, the conditional probability table 44b based on the prior distribution 44a is a conditional probability table generated based on the worker's experience, information on similar fields, etc.

[0078] 8, the conditional probability table generating unit 45 acquires the observed data 41a acquired by the observed data acquiring unit 41. The data frequency β of the observed data 41a jk is expressed by the formula (3). jk is expressed by a multinomial distribution. jk The maximum likelihood estimator φ for jk is expressed by equation (4). N j is the total number of observation data 41a in the discrete interval j, and N jk is the data frequency β of the observation data 41a in the discrete interval j, k. jk is.

[0079]

number

[0080]

number

[0081] The conditional probability table generation unit 45 generates the conditional probability table based on the observation data 41 a acquired by the observation data acquisition unit 41 and the parameter α of the prior distribution 44 a stored in the prior distribution storage unit 44. jk The posterior distribution 45a is calculated using Bayesian estimation based on the above. The posterior distribution 45a is a Dirichlet distribution configured with the same discrete intervals as the prior distribution 44a.

[0082] Furthermore, the conditional probability table generation unit 45 calculates the expected value θb of the posterior distribution 45a based on the calculated posterior distribution 45a. jk Here, the conditional probability table M1 is generated based on the discrete intervals determined by the interval determination unit 42. In other words, the conditional probability table M1 generated by the conditional probability table generation unit 45 is configured with the same discrete intervals as the conditional probability table 44b generated based on the prior distribution 44a. The generated conditional probability table M1 is shown in the upper part of Figure 10. The conditional probability table M1 has the value θb as the element value of each discrete interval. jk is stored.

[0083] The lower part of Fig. 10 shows a diagram for explaining in detail the discrete interval j of the parent factor A in the upper part of Fig. 10. Each element value θb of the conditional probability table M1 in the upper part of Fig. 10 jk is the expected value θb of the posterior distribution 45a in the lower part of FIG. jk Matches.

[0084] Expected value θb of posterior distribution 45a jk is expressed by equation (5). In equation (5), W j is the weight. Na jk is the parameter α of the prior distribution 44a jk is the value of the discrete interval j in j is the parameter α of the prior distribution 44a in the discrete interval j. jk is the sum of N jk is the data frequency β of the observation data 41a jk is the value of the discrete interval j in N j is the total number of data in the observation data 41a in the discrete interval j.

[0085]

number

[0086] That is, the conditional probability table generation unit 45 calculates the parameter α of the observation data 41a and the prior distribution 44a. jk Based on this, a posterior distribution 45a is calculated using Bayesian estimation, and the expected value θb jk A conditional probability table M1 is generated with each element value as follows:

[0087] Here, the conditional probability table generating unit 45 does not simply use Bayesian estimation, but uses weights W j In other words, the posterior distribution 45a is based on the observed data 41a and the weights W j The parameter α of the prior distribution 44a that takes into account jkFurthermore, the element values ​​Nb obtained in the process of generating the posterior distribution 45a by the conditional probability table generating unit 45 are calculated using Bayesian estimation. jk is expressed by equation (6). jk corresponds to the numerator of equation (5).

[0088]

number

[0089] For comparison, the weight W j The expected value θb of the posterior distribution 45a when not applying jk is expressed by equation (7).

[0090]

number

[0091] In this embodiment, as shown in equation (5), the weight W j Using this, the expected value θb of the posterior distribution 45a jk The expected value θb of the posterior distribution 45a in this embodiment is calculated. jk represents the prior distribution 44a jk , Na j Weight W j In other words, the weight W j is the parameter α of the prior distribution 44a for the observed data 41a. jk From equation (5), the weight W j The larger the parameter α of the prior distribution 44a for the observed data 41a, the jk The influence ratio of is high, and the weight W j The smaller the parameter α of the prior distribution 44a for the observed data 41a, jk The impact rate of

[0092] Weight W j is expressed by equation (8). In equation (8), K j is the weight W j It is a coefficient to represent Na jis the parameter α of the prior distribution 44a in the discrete interval j. jk is the sum of N j is the total number of observation data 41a in the discrete interval j. In other words, the weight W j Na j , N j It is expressed by:

[0093]

number

[0094] When the amount of observed data 41a is small, the conditional probability table generation unit 45 calculates the observed data 41a and the parameter α jk Based on this, we use Bayesian estimation to calculate the expected value θb of the posterior distribution 45a. jk It is preferable to generate a conditional probability table M1 in which each element value is . If a large amount of observation data 41a can be acquired, the conditional probability table generation unit 45 may generate the conditional probability table M1 based only on the observation data 41a. Therefore, the conditional probability table generation unit 45 is configured to be able to select between generating the conditional probability table M1 using Bayesian estimation and generating the conditional probability table M1 using maximum likelihood estimation based on the observation data 41a.

[0095] In FIG. 8, the weight storage unit 46 stores the weight W j Store the weight W j can be set to different values ​​for each discrete interval of the factor value of the parent factor A, or can be set to the same value. As mentioned above, from equation (5), the weight W j The larger the parameter α of the prior distribution 44a for the observed data 41a, the jk The influence ratio of is high, and the weight W j The smaller the parameter α of the prior distribution 44a for the observed data 41a, jk For example, the weight storage unit 46 stores a plurality of weights W as shown in equation (9). j_L ,W j_M ,W j_S Store.

[0096]

number

[0097] Weight W j The meaning of the weight W will be explained in detail using specific numerical values. j The expected value θb of the posterior distribution 45a without using jk Here, N j4 is "80" and Na j When is "100", the parameter α of the prior distribution 44a jk Compare the case of {1,2,2,5} with the case of {100,200,200,500}. The parameter α of the prior distribution 44a jk The ratios of these are all {1:2:2:5}.

[0098] Parameter α of prior distribution 44a jk If {1,2,2,5} (α j4 = 5) the expected value θb of the posterior distribution 45a j4 is "0.7727" as shown in equation (10). The calculation formula is "(5 + 80) / (10 + 100)". The parameter α of the prior distribution 44a jk When {100,200,200,500} (α j4 = 500) the expected value θb of the posterior distribution 45a j4 is "0.5272" as shown in formula (11). The calculation formula is "(500+80) / (1000+100)".

[0099]

number

[0100]

number

[0101] Thus, the parameter α of the prior distribution 44a jkEven if the ratio of the parameters is the same {1:2:2:5}, the parameter α of the prior distribution 44a jk Depending on the magnitude of the absolute value of j4 As shown in equation (10), the parameter α of the prior distribution 44a jk The smaller the absolute value of , the greater the expected value θb of the posterior distribution 45a. j4 is the data frequency β of the observation data 41a jk The maximum likelihood estimator φ for j4 On the other hand, as shown in equation (11), the parameter α jk The larger the absolute value of , the greater the expected value θb of the posterior distribution 45a. j4 is the expected value θa of the prior distribution 44a j4 The value is close to 0.5 (= 5 / 10).

[0102] And the weight W j can be regarded as the confidence of the prior distribution 44a. In other words, when the confidence of the prior distribution 44a is high, the weight W j In this case, the expected value θb of the posterior distribution 45a j4 is the expected value θa of the prior distribution 44a j4 On the other hand, when the confidence level of the prior distribution 44a is low, the weight W j In this case, the expected value θb of the posterior distribution 45a j4 is the data frequency β of the observation data 41a jk The maximum likelihood estimator φ for j4 In this way, the weight W j By adjusting the expected value θb of the posterior distribution 45a, j4 can be set to a desired value.

[0103] 8, the knowledge model storage unit 22 stores the conditional probability table M1 generated by the conditional probability table generation unit 45 as the knowledge model M representing the relationship between a parent factor A and a child factor D.

[0104] The model editing processor 47 is configured to receive input from an operator and to be able to edit the conditional probability table M1 constituting the knowledge model M stored in the knowledge model storage unit 22 in accordance with the operator's input. The model editing processor 47 makes it possible to adjust each element value of the conditional probability table M1 shown in the upper part of Fig. 10 in accordance with the operator's input. Note that the model editing processor 47 can also create the conditional probability table M1 from scratch in accordance with the operator's input.

[0105] Here, in addition to the above processing, when the observation data acquisition unit 41 acquires new observation data 41a, the conditional probability table generation unit 45 can generate a new conditional probability table M1 and update the knowledge model M stored in the knowledge model storage unit 22.

[0106] In this case, the prior distribution storage unit 44 stores the element values ​​Nb obtained in the process of generating the posterior distribution 45a by the conditional probability table generation unit 45. jk (expressed in Equation (6)) is used as the parameter α of the following prior distribution 44a jk Then, the conditional probability table generating unit 45 stores the parameter α jk and the observation data 41a newly acquired by the observation data acquisition unit 41. The conditional probability table generation unit 45 calculates the expected value θb of the posterior distribution 45a based on the newly calculated posterior distribution 45a. jk A conditional probability table M1 can be generated in which each element value is

[0107] 11 to 13, the procedure for updating the conditional probability table M1 constituting the knowledge model M will be described in detail. As shown in FIG. 11, when there is no observed data 41a, the parameter α jk In other words, the element values ​​of the conditional probability table M1 are calculated based on the expected value θa of the prior distribution 44a. jk At this point, the conditional probability table M1 constituting the knowledge model M stored in the knowledge model storage unit 22 is the parameter αjk The conditional probability table M1 (44b) is defined by the conditional probability table M1 (44b) expressed only by

[0108] Next, assume that observed data 41a is acquired. As shown in FIG. 12, the conditional probability table generation unit 45 calculates the parameter α jk Based on the observation data 41a, the parameter γ of the posterior distribution 45a is calculated using Bayesian estimation. jk Calculate the expected value θb of the posterior distribution 45a jk Then, the conditional probability table generation unit 45 generates the expected value θb jk Then, the conditional probability table generating unit 45 updates the conditional probability table M1 constituting the knowledge model M stored in the knowledge model storage unit 22.

[0109] Next, the conditional probability table generation unit 45 calculates each element value Nb obtained in the process of generating the posterior distribution 45a. jk The parameter α of the new prior distribution 44a is stored in the prior distribution storage unit 44. jk That is, as shown in FIG. 13, the parameter α jk However, each element value Nb obtained in the previous generation process of the posterior distribution 45a jk Then, suppose that new observation data 41a is acquired. The conditional probability table generation unit 45 calculates the parameter α jk and the new observation data 41a, a new posterior distribution 45a is calculated, and the expected value θb' of the new posterior distribution 45a is calculated. jk Then, the conditional probability table generation unit 45 generates the expected value θb' of the new posterior distribution 45a. jk Then, the conditional probability table generating unit 45 updates the conditional probability table M1 constituting the knowledge model M stored in the knowledge model storage unit 22 again.

[0110] 7. Configuration of specific cutting resistance output unit 23 5, the specific cutting resistance output unit 23 performs forward probabilistic inference using the conditional probability table M1 that constitutes the knowledge model M, thereby outputting an estimated value of the specific cutting resistance. The specific cutting resistance output unit 23 also performs backward probabilistic inference using the knowledge model M, thereby outputting factor values ​​for the machining conditions of the cutting work, the specifications of the tool T, and the specifications of the workpiece W. Each process performed by the specific cutting resistance output unit 23 will be described in detail below.

[0111] The process of performing forward probability inference by the specific cutting resistance output unit 23 will be described with reference to Fig. 14. The specific cutting resistance output unit 23 acquires factor values ​​of parent factors A, B, and C (factor value acquisition step S1).

[0112] Next, the specific cutting resistance output unit 23 performs forward probabilistic inference using the conditional probability table M1 constituting the knowledge model M to obtain the probabilities P1(1) to P1(8), P1(11) to P1(18), P2(1) to P2(8), P2(11) to P2(18), P3(1) to P3(8), and P3(11) to P3(18) (shown in Table 1) of the factor values ​​of the child factor D corresponding to the obtained factor values ​​of the parent factors A, B, and C (probability obtaining step S2). For example, if the factor values ​​of the parent factors A, B, and C are Aa1, Ab1, B1, and C1, the specific cutting resistance output unit 23 obtains the probability P1(1) of the factor value D1 of the child factor D, the probability P2(1) of the factor value D2 of the child factor D, and the probability P3(1) of the factor value D3 of the child factor D.

[0113] Next, the specific cutting resistance output unit 23 generates and outputs an estimated value of the specific cutting resistance based on the acquired probabilities P1(1), P2(1), and P3(1) (output step S3). For example, the specific cutting resistance output unit 23 can use the expected value obtained from the probabilities P1(1), P2(1), and P3(1) of the factor values ​​D1, D2, and D3 of the child factor D as the estimated value of the specific cutting resistance. The specific cutting resistance output unit 23 can also use the factor value of the child factor D with the highest probability as the estimated value of the specific cutting resistance. The specific cutting resistance output unit 23 can also calculate the estimated value of the specific cutting resistance based on the factor values ​​of a predetermined number of child factors D in descending order of probability.

[0114] In this way, the specific cutting resistance output unit 23 can output an estimated value of the specific cutting resistance corresponding to the machining conditions (cutting depth, cutting speed, feed rate, etc.) as the input parent factors A, B, and C, the specifications of the tool T, and the specifications of the workpiece W.

[0115] Next, the process of performing backward probabilistic inference by the specific cutting resistance output unit 23 will be described with reference to Fig. 15. The specific cutting resistance output unit 23 acquires a target factor value of the child factor D, which is a target value of the specific cutting resistance (factor value acquisition step S11).

[0116] Next, the specific cutting resistance output unit 23 performs backward probabilistic inference using the conditional probability table M1 that constitutes the knowledge model M (probability acquisition step S12). In the backward probabilistic inference, the specific cutting resistance output unit 23 further uses the probability tables M2, M3, and M4 of each of the factors A, B, and C. Then, using the conditional probability table M1 and each of the probability tables M2, M3, and M4, the specific cutting resistance output unit 23 can obtain the probability of each factor value of the parent factors A, B, and C that corresponds to the target factor value of the specific cutting resistance.

[0117] Next, the specific cutting resistance output unit 23 generates and outputs each factor value of the parent factors A, B, and C, i.e., factor values ​​for the cutting conditions, the specifications of the tool T, and the specifications of the workpiece W, based on the calculated probability of each factor value of the parent factors A, B, and C (output step S13). In this way, the specific cutting resistance output unit 23 can output the factor values ​​of the parent factors A, B, and C corresponding to the input target factor value of the specific cutting resistance, i.e., factor values ​​for the cutting conditions, the specifications of the tool T, and the specifications of the workpiece W.

[0118] 8. Processing of the management device 2 when focusing on the cutting depth to be evaluated The processing of the management device 2 shown in Fig. 3, particularly the specific cutting resistance generating unit 12, the stability limit diagram generating unit 13, and the chatter occurrence determining unit 14, will be described with a focus on the cutting depth to be evaluated. The cutting depth to be evaluated is the cutting depth to be evaluated. In the following, an example will be described in which the cutting depth to be evaluated is 0.55 mm.

[0119] The specific cutting resistance generator 12 generates an estimated value of the specific cutting resistance using the conditional probability table M1 shown in Table 1. Here, the evaluation target cutting amount of 0.55 mm is included in the cutting amount range Aa2 of 0.5 to 0.67 mm. Therefore, in the conditional probability table M1 shown in Table 1, it corresponds to the cutting amount range Aa2, which is the factor value of the cutting amount Aa of the parent factor A. Then, using the conditional probability table M1, the specific cutting resistance generator 12 generates an estimated value of the specific cutting resistance D, which is a child factor, corresponding to the cutting amount range Aa2 of the cutting amount, which is the parent factor Aa, and the factor values ​​of the other parent factors Ab, B, and C.

[0120] Next, the stability limit diagram generating unit 13 generates a stability limit diagram using the compliance transfer function stored in the compliance transfer function storage unit 11 and the specific cutting resistance generated by the specific cutting resistance generating unit 12. The stability limit diagram generating unit 13 generates stability limit diagrams SD1 to SD4 corresponding to the cutting-in amount sections Aa1 to Aa4. In this example, the cutting-in amount to be evaluated is 0.55 mm, so the stability limit diagram SD2 corresponding to the cutting-in amount section Aa2 is generated.

[0121] Next, the chatter occurrence determination unit 14 determines whether chatter will occur for the evaluation target cutting amount and evaluation target rotation speed using the stability limit diagram SD2 generated by the stability limit diagram generation unit 13. In other words, the chatter occurrence determination unit 14 determines that chatter will not occur if the position corresponding to the evaluation target cutting amount and evaluation target rotation speed is in the lower region of the generated stability limit diagram SD2.

[0122] 9.Effects According to the above-described machining condition setting support system 1, the stability limit diagram generating unit 13 can generate the stability limit diagrams SD1 to SD4 corresponding to each of the cutting-in amount sections Aa1 to Aa4 when the cutting-in amount that can be taken as a machining condition is divided into a plurality of sections. In other words, there is not one type of stability limit diagram SD1 to SD4, but multiple types corresponding to each of the cutting-in amount sections Aa1 to Aa4.

[0123] It was discovered that the stability limit diagrams SD1 to SD4 behave differently depending on the cutting depth, and by finding the relationship between the cutting depth and the stability limit diagrams SD1 to SD4, it became possible to generate the stability limit diagrams SD1 to SD4 corresponding to each of the divided cutting depth intervals Aa1 to Aa4, as described above. Therefore, the stability limit diagrams SD1 to SD4 can be expressed with higher accuracy than before.

[0124] The chatter occurrence determination unit 14 is configured to determine whether chatter occurs for the cutting depth to be evaluated and the rotational speed to be evaluated as machining conditions. This determination uses the target stability limit diagram SD1-SD4 corresponding to the cutting depth to be evaluated. As described above, there are multiple types of stability limit diagrams SD1-SD4 corresponding to the respective divided cutting depth sections Aa1-Aa4. Therefore, for the determination, the stability limit diagram SD1-SD4 corresponding to the cutting depth section Aa1-Aa4 including the cutting depth to be evaluated is used. Then, using the stability limit diagram SD1-SD4, it is determined whether chatter occurs for the cutting depth to be evaluated and the rotational speed to be evaluated. In this way, chatter determination is performed using the stability limit diagram SD1-SD4 corresponding to the cutting depth to be evaluated. This makes it possible to determine more appropriate machining conditions.

[0125] The machining condition setting support system 1 also includes a processor 2a and a storage device 2b. The storage device 2b includes a compliance transfer function storage unit 11 that stores a compliance transfer function, which is a transfer function of a response displacement when an excitation force is applied to at least one of the tool T and the workpiece W. The processor 2a further includes a specific cutting resistance output unit 23 that is configured to output an estimated value of the specific cutting resistance, which is a value obtained by dividing the cutting resistance by the cutting cross-sectional area and corresponds to each of the divided cutting depth intervals Aa1 to Aa4, based on at least the cutting depth.

[0126] The stability limit diagram generating unit 13 is configured to generate stability limit diagrams SD1 to SD4 of the target corresponding to the cutting-in amount intervals Aa1 to Aa4 including the cutting-in amount to be evaluated, based on the compliance transfer function and an estimated value of the target specific cutting resistance corresponding to the cutting-in amount to be evaluated as a machining condition. The chatter occurrence determining unit 14 is configured to determine whether chatter will occur, using the stability limit diagrams SD1 to SD4 of the target generated by the stability limit diagram generating unit 13.

[0127] In this way, the stability limit diagrams SD1 to SD4 are generated using the compliance transfer function and the estimated value of the specific cutting resistance. The estimated value of the specific cutting resistance corresponds to the depth of cut to be evaluated. Therefore, the generated stability limit diagrams SD1 to SD4 are more accurate.

[0128] Furthermore, the storage device 2b constituting the machining condition setting support system 1 further includes a knowledge model storage unit 22 that stores the knowledge model M. The knowledge model M is configured to include a conditional probability table M1 in which at least the cutting depth, the specifications of the tool T, and the specifications of the workpiece W among the machining conditions are defined as parent factors A, B, and C, specific cutting resistance is defined as child factor D, and probabilities P1(1) to P1(8), P1(11) to P1(18), P2(1) to P2(8), P2(11) to P2(18), P3(1) to P3(8), and P3(11) to P3(18) that correspond to the factor value of child factor D when the factor values ​​of parent factors A, B, and C are used as conditions.

[0129] The specific cutting resistance output unit 23 is configured to output an estimated value of the specific cutting resistance as a child factor D by performing forward probabilistic inference using the conditional probability table M1 when it obtains the factor values ​​of the parent factors A, B, and C.

[0130] As described above, in the processing condition setting support system 1, the conditional probability table M1 is configured as the knowledge model M. The conditional probability table M1 is defined by the probability of the factor value of the child factor D when the factor values ​​of the parent factors A, B, and C are used as conditions. In other words, the conditional probability table M1 does not directly relate the factor values ​​of the parent factors A, B, and C and the child factor D to each other, but instead represents them using probabilities P1(1) to P1(8), P1(11) to P1(18), P2(1) to P2(8), P2(11) to P2(18), P3(1) to P3(8), and P3(11) to P3(18). This allows for flexible definition of the relationships between factors.

[0131] Parent factors A, B, and C include the machining conditions for cutting, the specifications of tool T, and the specifications of workpiece W. Child factor D is the specific cutting resistance. By making knowledge model M a conditional probability table M1, even if there are many types of parent factors A, B, and C, it becomes easy to define the probabilities P1(1) to P1(8), P1(11) to P1(18), P2(1) to P2(8), P2(11) to P2(18), P3(1) to P3(8), and P3(11) to P3(18) that correspond to the factor value of child factor D, which is the specific cutting resistance, when the factor values ​​of these multiple parent factors A, B, and C are used as conditions.

[0132] Then, when the specific cutting resistance output unit 23 acquires factor values ​​relating to the machining conditions, the specifications of the tool T, and the specifications of the workpiece W as parent factors A, B, and C, it can obtain an estimated value of the specific cutting resistance as a child factor D by performing forward probabilistic inference using the conditional probability table M1 that constitutes the knowledge model M.

[0133] Therefore, according to the machining condition setting support system 1, by constructing and utilizing a knowledge model M relating to the specific cutting resistance, it is possible to estimate the specific cutting resistance with high accuracy.

[0134] In addition, the conditional probability table M1 discretizes the cutting amount as the parent factor A into each of the divided cutting amount intervals Aa1 to Aa4. Each of the cutting amount intervals Aa1 to Aa4 is set wide in the range where the change in specific cutting resistance relative to the cutting amount is small, and is set narrow in the range where the change in specific cutting resistance relative to the cutting amount is large.

[0135] In particular, the change in specific cutting resistance increases as the cutting depth decreases and decreases as the cutting depth increases. The cutting depth sections Aa1 to Aa4 are set wider as the cutting depth decreases and narrower as the cutting depth increases. Therefore, the stability limit diagrams SD1 to SD4 can be generated with high accuracy.

[0136] Furthermore, the cutting resistance F generated in cutting is expressed by a principal force F1, a thrust force F2, and a feed force F3. In the machining condition setting support system 1, the specific cutting resistance corresponds to the value obtained by dividing the thrust force F2 that constitutes the cutting resistance F by the cutting cross-sectional area. In this way, by setting the specific cutting resistance to a value related to the thrust force F2, which has a significant effect on chatter, the specific cutting resistance related to chatter can be derived with high accuracy.

[0137] The conditional probability table M1 constituting the knowledge model M is generated using Bayesian estimation. That is, the storage device 2b constituting the processing condition setting support system 1 stores the parameter α of the prior distribution 44a set in advance to determine the conditional probability table M1. jk Furthermore, the processor 2a constituting the machining condition setting support system 1 includes an observation data acquisition unit 41 that acquires observation data 41a obtained by performing actual cutting or a simulation of cutting, the observation data 41a being about the factor values ​​of parent factors A, B, and C and the factor value of child factor D, and a parameter α jk and calculating a posterior distribution 45a using Bayesian estimation based on the observed data 41a, and calculating the expected value θb of the posterior distribution 45a. jkand a conditional probability table generating unit 45 that generates a conditional probability table M1 having element values ​​of the above. The knowledge model storage unit 22 stores the knowledge model M configured by the conditional probability table M1 generated by the conditional probability table generating unit 45.

[0138] In this way, by using Bayesian estimation in generating the conditional probability table M1, the prior distribution 44a and the observed data 41a can be effectively used. In other words, by combining the prior distribution 44a as predefined knowledge with the observed data 41a, the accuracy of the conditional probability table M1 can be improved.

[0139] In addition, the storage device 2b constituting the processing condition setting support system 1 calculates the parameter α of the prior distribution 44a for the observation data 41a in the Bayesian estimation. jk Weight W representing the influence ratio of j The conditional probability table generating unit 45 is provided with a weight storage unit 46 for storing the observed data 41a and the weights W j The parameter α of the prior distribution 44a that takes into account jk The posterior distribution 45a is calculated using Bayesian estimation based on the jk A conditional probability table M1 is generated with each element value as follows:

[0140] When generating the conditional probability table M1 using Bayesian estimation, the accuracy of the prior distribution 44a may not be sufficient to generate the conditional probability table M1 with high accuracy. j Using the expected value θb of the posterior distribution 45a jk By calculating the above, a conditional probability table M1 according to the confidence of the prior distribution 44a can be generated.

[0141] Then, when the conditional probability table generation unit 45 generates the conditional probability table M1 using Bayesian estimation and a large amount of observation data 41a is obtained, the expected value θb of the posterior distribution 45a jkIn addition, when the conditional probability table generation unit 45 sequentially updates the conditional probability table M1 using Bayesian estimation, a large amount of observation data 41a can be secured, and the expected value θb of the posterior distribution 45a can be increased. jk Therefore, if a large amount of observation data 41a can be secured, a highly accurate conditional probability table M1 can be generated by increasing the influence rate of highly reliable observation data 41a.

[0142] And the weight W j can be set to a different value for each factor value of parent factors A, B, and C, i.e., for each discrete interval j of parent factors A, B, and C. j represents the confidence of the prior distribution 44a. The confidence may differ depending on the discrete interval j of the parent factors A, B, and C. For example, in a discrete interval j that the operator has had many experiences with, the prior distribution 44a can be determined with high confidence. However, in other cases, the prior distribution 44a may not be determined with high confidence. In such cases, the weight W j By setting for each discrete interval j, an appropriate conditional probability table M1 can be generated.

[0143] Furthermore, the processor 2a constituting the machining condition setting support system 1 includes a recommended condition determination unit 15 that determines recommended values ​​of machining conditions using a stability limit diagram, and an instruction unit 16 that instructs the recommended values ​​of the machining conditions determined by the recommended condition determination unit 15. In this way, by instructing the recommended values ​​of the machining conditions, the worker or manager can grasp the appropriate machining conditions.

[0144] Furthermore, the processing devices 4 and 5 may be configured to be able to communicate with the processor 2a, acquire recommended values ​​of the processing conditions taught by the teaching unit 16, and perform cutting processing using the acquired recommended values ​​of the processing conditions. In this way, the processing devices 4 and 5 autonomously perform cutting processing using the taught processing conditions, thereby realizing desired cutting processing without human intervention.

[0145] (Embodiment 2) The configuration of the processing condition setting support system 100 of the second embodiment will be described with reference to Fig. 16. In Fig. 16, the same components as those of the first embodiment are denoted by the same reference numerals.

[0146] 16, the machining condition setting support system 100 is configured by incorporating the processor 2a and storage device 2b constituting the management device 2 in embodiment 1 into each of the machining devices 4 and 5. That is, each of the machining devices 4 and 5 has the functions of the processor 2a and storage device 2b in embodiment 1 as a standalone unit. The machining devices 4 and 5 are configured to acquire recommended values ​​of machining conditions taught by the teaching unit 16 of the processor 2a, and to perform cutting using the acquired recommended values ​​of machining conditions.

[0147] The processing devices 4 and 5 applicable to this embodiment are various processing devices that perform cutting, such as a lathe, a machining center, a milling machine, a gear processing machine, and a boring machine.

[0148] (others) In the machining condition setting support system 100 of the second embodiment, all of the functions of the processor 2a and the storage device 2b of the first embodiment are incorporated into the machining devices 4 and 5, but some of the functions of the processor 2a and the storage device 2b may also be incorporated. In this case, the system is configured with a management device 2 and the machining devices 4 and 5 that form a network, and the management device 2 has some of the functions of the processor 2a and the storage device 2b, and the machining devices 4 and 5 incorporate some of the remaining functions. [Explanation of symbols]

[0149] 1,100 Processing condition setting support system 2a processor 2b Storage device 13 Stability limit diagram generation section 14 Chatter occurrence determination unit Aa1~Aa4 Cutting depth range SD1~SD4 Stability limit diagram T-tool W Workpiece

Claims

1. A machining condition setting support system including a processor, which evaluates or sets machining conditions including at least a cutting depth and a rotation speed when performing cutting while rotating at least one of a tool and a workpiece, The processor: a stability limit diagram generating unit that divides the depth of cut that can be taken as the machining condition into a plurality of sections, and that is capable of generating each stability limit diagram corresponding to each divided depth of cut section, the stability limit diagram defining the relationship between the rotation speed of the tool or the workpiece and the maximum depth of cut that does not cause chatter; a chatter occurrence determination unit configured to determine whether chatter occurs in the case of an evaluation target cutting-in amount and an evaluation target rotation speed included in the machining conditions, using a target stability limit diagram corresponding to an evaluation target cutting-in amount among the plurality of stability limit diagrams that can be generated; A processing condition setting support system equipped with the above.

2. the processor and a storage device; The storage device a compliance transfer function storage unit that stores a compliance transfer function that is a transfer function of a response displacement when an excitation force is applied to at least one of the tool and the workpiece, The processor further comprises: a specific cutting resistance output unit configured to output an estimated value of each specific cutting resistance, the specific cutting resistance being a value obtained by dividing the cutting resistance by the cutting cross-sectional area, the specific cutting resistance corresponding to each of the divided cutting depth sections, based on at least the cutting depth; the stability limit diagram generating unit is configured to generate the stability limit diagram of an object corresponding to the cutting-in amount section including the cutting-in amount to be evaluated, based on the compliance transfer function and an estimated value of the specific cutting resistance of an object corresponding to the cutting-in amount to be evaluated as the machining condition, 2. The machining condition setting support system according to claim 1, wherein the chatter occurrence determination unit is configured to determine whether chatter occurs or not, using the stability limit diagram of the target generated by the stability limit diagram generation unit.

3. The storage device further comprises: a knowledge model storage unit for storing a knowledge model, the knowledge model is configured to include a conditional probability table in which at least the cutting depth, the tool specifications, and the workpiece specifications among the machining conditions are parent factors, specific cutting resistance is a child factor, and a probability that the factor value of the child factor corresponds to a conditional factor value of the parent factor is defined; 3. The machining condition setting support system according to claim 2, wherein the specific cutting resistance output unit is configured to, when acquiring the factor value of the parent factor, output an estimated value of the specific cutting resistance as the child factor by performing forward probabilistic inference using the conditional probability table.

4. In the conditional probability table, the cutting amount as the parent factor is discretized into each of the divided cutting amount intervals, 4. The machining condition setting support system according to claim 3, wherein each of the cutting depth ranges is set wide in a range where the change in the specific cutting resistance relative to the cutting depth is small, and is set narrow in a range where the change in the specific cutting resistance relative to the cutting depth is large.

5. The cutting resistance generated in the cutting process is represented by a principal force, a thrust force, and a feed force, 4. The machining condition setting support system according to claim 3, wherein the specific cutting resistance corresponds to a value obtained by dividing the thrust force constituting the cutting resistance by a cutting cross-sectional area.

6. the storage device further includes a prior distribution storage unit that stores parameters of a prior distribution that is preset to determine the conditional probability table; The processor further comprises: an observation data acquisition unit configured to acquire observation data obtained by performing the actual cutting process or a simulation of the cutting process, the observation data being about factor values ​​of the parent factors and factor values ​​of the child factors; a conditional probability table generation unit configured to calculate a posterior distribution using Bayesian estimation based on the parameters of the prior distribution and the observation data, and to generate the conditional probability table in which each element value is an expected value of the posterior distribution; Equipped with 4. The machining condition setting support system according to claim 3, wherein the knowledge model storage unit stores the knowledge model configured by the conditional probability table generated by the conditional probability table generation unit.

7. the storage device further includes a weight storage unit configured to store weights representing influence ratios of parameters of the prior distribution to the observation data in the Bayesian estimation; 7. The machining condition setting support system according to claim 6, wherein the conditional probability table generation unit is configured to calculate the posterior distribution using the Bayesian estimation based on the observation data and parameters of the prior distribution taking into account the weights, and to generate the conditional probability table in which each element value is an expected value of the posterior distribution.

8. The processor further comprises: a recommended condition determination unit that determines recommended values ​​of the machining conditions using the stability limit diagram; an instruction unit that instructs the recommended values ​​of the machining conditions determined by the recommended condition determination unit; The processing condition setting support system according to any one of claims 1 to 7, comprising:

9. Further, a processing device configured to be able to communicate with the processor or configured with the processor incorporated therein is provided, 9. The machining condition setting support system according to claim 8, wherein the machining device is configured to acquire the recommended values ​​of the machining conditions taught by the teaching unit, and to perform the cutting processing by applying the acquired recommended values ​​of the machining conditions.

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