Processing support system

Abstract

To provide a processing support system for outputting a plurality of sets of output factor information only by inputting one set of input factor information.SOLUTION: A processing support system 1 that utilizes a knowledge model M is provided with an arithmetic processing unit 3 for performing arithmetic operation by using the knowledge model M. The arithmetic processing unit 3 includes a basic input factor information acquisition part 3a for acquiring one set of basic input factor information, an input factor information generation part 3b for generating a plurality of sets of variation input factor information on the basis of the one set of basic input factor information, an output factor information generation part 3c for generating one set of basic output factor information and a plurality of sets of variation output factor information on the basis of the knowledge model M, a selection part 3d for selecting at least one set of output factor information for teaching from the plurality of sets of variation output factor information, and a teaching processing part 3e for teaching the one set of basic output factor information and teaching at least the selected one set of output factor information for teaching.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a machining support system.

Background Art

[0002] Conventionally, in order to determine machining conditions in industrial machines, it is known to database the knowledge of skilled workers and utilize the database. For example, Patent Document 1 describes a database knowledge model. The knowledge model is a model composed of a plurality of factors defined by various technical terms and the relationships between the factors. And it is said that the optimization of machining conditions can be achieved using the knowledge model.

[0003] When workpiece information, target cycle time, target machining accuracy, etc. are input as input factor information using the machining support system using the above knowledge model, cycle time, machining conditions, and machining accuracy prediction are output as output factor information.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] According to the above technology, when a set of input factor information including workpiece information, target cycle time, target machining accuracy, etc. is input, only one set of output factor information corresponding to this set of input factor information is output. Therefore, when multiple patterns of machining conditions are required or when the customer's requirements change, multiple sets of input factor information need to be input, so the input operation is troublesome.

[0006] This invention has been made in view of the above problems, and aims to provide a processing support system that can output multiple sets of output factor information by simply inputting one set of input factor information. [Means for solving the problem]

[0007] One aspect of the present invention is, A machining support system that utilizes a knowledge model that defines the relationships between factors in the machining of a workpiece by a machining device, A storage device for storing the aforementioned knowledge model, A processing unit that performs calculations using the aforementioned knowledge model, Equipped with, The aforementioned arithmetic processing unit is A basic input factor information acquisition unit that acquires a set of basic input factor information, An input factor information generation unit generates multiple sets of variation input factor information that are different from the set of basic input factor information based on the set of basic input factor information, An output factor information generation unit generates, based on the knowledge model, a set of basic output factor information corresponding to the set of basic input factor information, and a set of variation output factor information corresponding to the set of variation input factor information. A selection unit that selects at least one set of teaching output factor information from the aforementioned multiple sets of variation output factor information, A teaching processing unit that teaches the aforementioned set of basic output factor information and teaches at least the selected set of teaching output factor information, Equipped with 、 The aforementioned set of basic input factor information includes the target machining accuracy, The selection unit selects at least one set of teaching output factor information before and after the target machining accuracy from among the multiple sets of variation output factor information, based on the target machining accuracy. It is located in the processing support system. Furthermore, other embodiments of the present invention include: A machining support system that utilizes a knowledge model that defines the relationships between factors in the machining of a workpiece by a machining device, A storage device for storing the aforementioned knowledge model, A processing unit that performs calculations using the aforementioned knowledge model, Equipped with, The aforementioned arithmetic processing unit is A basic input factor information acquisition unit that acquires a set of basic input factor information, An input factor information generation unit generates multiple sets of variation input factor information that are different from the set of basic input factor information based on the set of basic input factor information, An output factor information generation unit generates, based on the knowledge model, a set of basic output factor information corresponding to the set of basic input factor information, and a set of variation output factor information corresponding to the set of variation input factor information. A selection unit that selects at least one set of teaching output factor information from the aforementioned multiple sets of variation output factor information, A teaching processing unit that teaches the aforementioned set of basic output factor information and teaches at least the selected set of teaching output factor information, Equipped with, The selection unit is part of a machining support system that selects at least one set of teaching output factor information before and after a predicted value change point from among the multiple sets of variation output factor information, based on the predicted value change point where the predicted value of the machining accuracy of the workpiece changes.

Advantages of the Invention

[0008] According to one aspect of the present invention and other embodiments a set of output factor information corresponding to a set of input factor information input and at least one set of output factor information for teaching are displayed. Thereby, by simply inputting a set of input factor information, a plurality of sets of output factor information can be obtained, so that the input work of the input factor information can be labor-saving.

[0009] As described above, according to the above aspect, a processing support system that outputs a plurality of sets of output factor information by simply inputting a set of input factor information can be provided.

Brief Description of the Drawings

[0010] [Figure 1] It is a diagram showing the configuration of a processing support system according to Embodiment 1. [Figure 2] It is a diagram showing an arithmetic processing unit, a storage device, an input device, and a display device according to Embodiment 1. [Figure 3] It is a diagram showing a knowledge network diagram expressing a knowledge model. [Figure 4] It is a plan view showing a processing machine. [Figure 5] It is a plan view showing a workpiece. [Figure 6] [[ID=…]] [Figure 7] [Figure 7] It is a flowchart showing an output factor information generation process. [Figure 8] It is a flowchart showing a selection process. [Figure 9]This diagram shows the relationship between the basic input factor, the variation input factor, the basic output factor, the variation output factor, and the instructional output factor. [Figure 10] This graph shows the changes in cycle time and runout depending on the rough finishing division method. [Figure 11] This is a flowchart showing the selection process in the machining support system according to Embodiment 2. [Figure 12] This graph shows the information of the teaching output factors selected before and after the change in roundness. [Figure 13] This is a flowchart showing the main routine of the machining support system according to Embodiment 5. [Figure 14] This is a flowchart of the machine learning process. [Modes for carrying out the invention]

[0011] (Embodiment 1) 1. Configuration of the processing support system The configuration of the machining support system of Embodiment 1 will be described with reference to Figures 1 and 2. The machining support system is applied to the machining field, which processes workpieces. The machining field includes, for example, cutting, grinding, electrical discharge machining, and press working. The machining support system according to this embodiment is a system that utilizes a knowledge model that describes the knowledge of technical information possessed by skilled personnel in the machining field.

[0012] As shown in Figure 1, the machining support system 1 comprises a storage device 2 and an arithmetic processing unit 3. An input device 4 and a display device 5 are connected to the arithmetic processing unit 3. The machining support system 1 forms a network with multiple machining devices 6. In other words, the multiple machining devices 6 and the machining support system 1 are configured to communicate with each other.

[0013] The memory device 2 stores the knowledge model M, machine configuration information of the processing device 6, etc. The arithmetic processing unit 3 performs calculations using the knowledge model M. The processing device 6 is, for example, a grinding machine, a turning machine, a machining center, a milling machine, a gear cutting machine, a boring machine, etc. In this embodiment, a grinding machine will be used as an example.

[0014] The machining support system 1 uses a knowledge model M to assist in the operation of the machining equipment 6, determine operating conditions, assess the machine state, and assess the state of the workpiece W.

[0015] In general, in the field of machining, operators determine machining conditions such as cutting speed and depth of cut per unit time by considering various information such as the material of the workpiece W, the material of the tool, the quality of the workpiece W, and the machining cycle time. In this case, a knowledge model M is a model of the operator's thought process when they acquire various input information and determine the machining conditions.

[0016] In other words, the knowledge model M defines factors such as the material of the workpiece W, the material of the tool, the quality of the workpiece W, the machining cycle time, the cutting speed, and the depth of cut, as well as each of the industrial technology elements that appear in the operator's thought process, and the relationships between these factors are defined.

[0017] For example, when the machining support system 1 determines the machining conditions of the machining device 6, if it obtains input factors such as the material and quality of the workpiece W, it can use a knowledge model M to output machining conditions such as cutting speed and depth of cut as output factors.

[0018] As shown in Figure 2, the arithmetic processing unit 3 comprises a basic input factor information acquisition unit 3a, an input factor information generation unit 3b, an output factor information generation unit 3c, a selection unit 3d, and a teaching processing unit 3e.

[0019] The basic input factor information acquisition unit 3a acquires basic input factor information from the input device 4. Basic input factor information is a set of multiple input factor information. Basic input factor information includes workpiece information (shape, material, etc. of workpiece W), target cycle time, target machining accuracy (target roundness, target chatter, target runout, target phase angle, target burn, etc.).

[0020] The input factor information generation unit 3b generates multiple sets of variation input factor information that are different from the set of basic input factor information from a set of basic input factor information. Variation input factor information is a set of multiple input factor information combined into one. Variation input factor information is different from basic input factor information. In other words, at least one input factor information included in the variation input factor information is different from the input factor information that constitutes the basic input factor information.

[0021] The output factor information generation unit 3c generates a set of basic output factor information and multiple sets of variation output factor information based on the knowledge model M stored in the storage device 2. The basic output factor information is a set of multiple output factor information and corresponds to the set of basic input factor information described above. Similarly, the variation output factor information is a set of multiple output factor information and corresponds to the variation input factor information described above. The basic output factor information and variation output factor information include cycle time, machining conditions (such as the manner of rough finishing division), machine configuration information of the machining device 6, and machining accuracy prediction results (such as roundness, chatter, runout, phase angle, burning, etc.).

[0022] The selection unit 3d selects at least one set of instructional output factor information from the multiple sets of variation output factor information generated by the output factor information generation unit 3c. The selection unit 3d may select one set of instructional output factor information, or it may select two or more sets.

[0023] The teaching processing unit 3e teaches the operator by displaying a set of basic output factor information and at least one selected set of teaching output factor information on the display device 5. The teaching processing unit 3e may also teach the operator the set of basic output factor information and at least one selected set of teaching output factor information via a device other than the display device 5, such as a printing device.

[0024] 2. Knowledge Network Diagram N Conceptually, the knowledge model M is represented in network form. An example of a knowledge network diagram N, which represents the knowledge model M in network form, will be explained with reference to Figure 3. In other words, the knowledge network diagram N is a graphical representation of the knowledge model M. In this example, we will use a knowledge network diagram N relating to the knowledge model M in the field of machining.

[0025] As shown in Figure 3, the knowledge network diagram N comprises multiple node shapes 21 and link shapes 22 connecting the node shapes 21. In this embodiment, the node shapes 21 are represented by letters enclosed in rectangles. In this embodiment, the link shapes 22 are represented by straight lines. However, the link shapes 22 may also be represented by arrow lines to indicate the direction of the definitions between factors. The node shapes 21 represent factors defined in industrial technology terminology in the knowledge model M.

[0026] Multiple factors can have either a technical hierarchical relationship (also called a superior-subordinate relationship) or a technical heterogeneous dependency relationship. In other words, the relationships between factors can be classified into the two types described above.

[0027] For example, machine specifications encompass rigidity, machine precision, responsiveness, and operating speed. In other words, as factors with a technical inclusion relationship, machine specifications are considered a higher-level conceptual factor, while rigidity, machine precision, responsiveness, and operating speed are considered lower-level conceptual factors. However, machine specifications may also include factors other than those mentioned above.

[0028] Examples of factors that have a technically heterogeneous dependency relationship include stiffness and cycle time. In the following, the relationship between two factors that have a technically inclusion relationship will be simply referred to as an inclusion relationship, and the relationship between two factors that have a technically heterogeneous dependency relationship will be simply referred to as a heterogeneous dependency relationship.

[0029] Link figures 22 represent the relationships between node figures 21. Link figures 22 represent the relationships between factors in the knowledge model M.

[0030] 3. Processing equipment 6 The processing apparatus 6 in this embodiment will be described with reference to Figures 4 and 5. In this embodiment, a grinding machine will be used as an example of the processing apparatus 6. However, the processing apparatus 6 is not limited to a grinding machine; any processing machine such as a lathe, machining center, milling machine, gear machine, boring machine, etc., can be appropriately selected.

[0031] The processing device 6 rotates the workpiece W around the center line C, rotates the grinding wheel 16 which is a rotating tool, and grinds the outer or inner surface of the workpiece W by bringing the grinding wheel 16 relatively closer to the workpiece W in a direction intersecting the axis of the workpiece W. The processing device 6 can be a table traverse type grinding machine, a grinding wheel base traverse type grinding machine, etc. Furthermore, the processing device 6 can be a cylindrical grinding machine, a cam grinding machine, etc.

[0032] In this embodiment, as shown in Figure 5, the workpiece W is, for example, a camshaft formed in an axial shape, and the outer surface of the workpiece W is the part to be machined. However, the shape of the workpiece W is not limited to a camshaft, and can be any shape such as a cylinder or a cylindrical shape with an inner surface. If the workpiece W is cylindrical, the inner surface of the workpiece W can be the part to be machined.

[0033] In this embodiment, the workpiece W is substantially rod-shaped and supported at both ends by workpiece support members.

[0034] The configuration of the processing apparatus 6 will be explained with reference to Figure 4. In this embodiment, the processing apparatus 6 is an example of a cam grinding machine with a grinding wheel base traverse. However, a table traverse type can also be used for the processing apparatus 6. The processing apparatus 6 mainly comprises a bed 11, a headstock 12, a tailstock 13, a traverse base 14, a grinding wheel base 15, a grinding wheel 16, a sizing device 17, a grinding wheel adjustment device 18, and a coolant device 19.

[0035] The bed 11 is fixed to the mounting surface. The headstock 12 is located on the upper surface of the bed 11, on the front side in the X-axis direction (lower side in Figure 4) and on one end side in the Z-axis direction (left side in Figure 4). The headstock 12 supports the workpiece W so that it can rotate around the Z-axis with respect to the center line C. The workpiece W is rotated by the drive of a motor 12a provided on the headstock 12. The tailstock 13 is located on the upper surface of the bed 11, opposite the headstock 12 in the Z-axis direction, that is, on the front side in the X-axis direction (lower side in Figure 4) and on the other end side in the Z-axis direction (right side in Figure 4). In other words, the headstock 12 and tailstock 13 rotatably support the workpiece W at both ends.

[0036] As shown in Figure 5, the workpiece W comprises a shaft portion 30 that is cylindrical with its longest axis in the Z-axis direction, and a plurality of cams 31 to 35 (five in this embodiment) arranged on the shaft portion 30. The plurality of cams are, in order from left to right in Figure 5, the first cam 31, the second cam 32, the third cam 33, the fourth cam 34, and the fifth cam 35. The first to fifth cams 31 to 35 are identical in shape and size. Although not shown in detail, the first to fifth cams 31 to 35 have a roughly egg shape when viewed from the Z-axis direction.

[0037] As shown in Figure 4, the traverse base 14 is mounted on the upper surface of the bed 11 so as to be movable in the Z-axis direction. The traverse base 14 is moved by the drive of a motor 14a mounted on the bed 11. The grinding wheel base 15 is mounted on the upper surface of the traverse base 14 so as to be movable in the X-axis direction. The grinding wheel base 15 is moved by the drive of a motor 15a mounted on the traverse base 14. The grinding wheel 16 is rotatably supported on the grinding wheel base 15. The grinding wheel 16 rotates by the drive of a motor 16a mounted on the grinding wheel base 15. The grinding wheel 16 is constructed by fixing multiple abrasive grains with a bonding agent.

[0038] The sizing device 17 measures the dimensions (diameter) of the workpiece W. The sizing device 17 functions as a detector 20 for obtaining the actual depth of cut in the workpiece W.

[0039] The grinding wheel correction device 18 corrects the shape of the grinding wheel 16. The grinding wheel correction device 18 is a device that performs truing of the grinding wheel 16. In addition to truing, or as an alternative to truing, the grinding wheel correction device 18 may also be a device that performs dressing of the grinding wheel 16. Furthermore, the grinding wheel correction device 18 also has a function to measure the dimensions (diameter) of the grinding wheel 16.

[0040] Here, truing is a reshaping process, which involves shaping the grinding wheel 16 to match the shape of the workpiece W when the grinding wheel 16 is worn down by grinding, and removing runout of the grinding wheel 16 caused by uneven wear. Dressing is a sharpening process, which involves adjusting the amount of abrasive grain protrusion and creating cutting edges for the abrasive grains. Dressing is a process to correct clogged, damaged, or chipped abrasive grains, and is usually performed after truing.

[0041] The coolant system 19 supplies coolant from the coolant nozzle to the grinding point of the workpiece W by the grinding wheel 16. The coolant system 19 cools the recovered coolant to a predetermined temperature and supplies it again to the grinding point. The coolant system 19 allows for adjustment of the coolant flow rate and supply timing. In Figure 4, reference numeral 19 indicates the position of the coolant nozzle. Although not shown, a temperature sensor, which acquires the temperature of the recovered coolant, may also be provided as a detector 20.

[0042] 4. Operation of the Machining Support System 1 Next, the operation of the machining support system 1 will be explained with reference to Figures 6 to 8.

[0043] 4-1. Input Processing (S1) As shown in Figure 6, when the machining support system 1 is started, input processing (S1) is executed. In input processing (S1), a set of basic input factor information is input to the basic input factor information acquisition unit 3a from the operator via an input device 4 such as a keyboard. However, a set of basic input factor information may also be input to the basic input factor information acquisition unit 3a from an external storage device such as a USB memory. As a result, the basic input factor information acquisition unit 3a acquires a set of basic input factor information.

[0044] 4-2. Input Factor Information Generation Process (S2) Next, the arithmetic processing unit 3 executes the input factor information generation process (S2). More specifically, the input factor information generation unit 3b generates multiple sets of variation input factor information based on each basic input factor information contained in the acquired set of basic input factor information.

[0045] For example, let us describe a case where a set of basic input factor information includes a rough finishing division method as one of the basic input factor information. As described above, the workpiece W in this embodiment is a camshaft. As for the rough finishing division method performed on the camshaft, for each of the 1st to 5th cams 31 to 35, there are two methods depending on whether or not rough finishing division is performed. 5 Several configurations are possible. The input factor information generation unit 3b is 25 Generate multiple sets of variation input factor information corresponding to the street patterns.

[0046] The input factor information generation unit 3b may generate variation input factor information for all factor information included in a set of basic input factor information, or it may generate variation input factor information for some of the factor information.

[0047] 4-3. Output Factor Information Generation Process (S3) Next, the output factor information generation unit 3c executes the output factor information generation process (S3). Figure 7 shows a flowchart of the output factor information generation process.

[0048] The output factor information generation unit 3c acquires the knowledge model M from the storage device 2 (S31). Subsequently, the output factor information generation unit 3c acquires a set of basic input factor information and multiple variation input factor information (S32).

[0049] Next, the output factor information generation unit 3c generates a set of basic output factor information from a set of basic input factor information based on the knowledge model M (S33). Furthermore, the output factor information generation unit 3c generates multiple sets of variation output factor information from multiple sets of variation input factor information based on the knowledge model M (S33).

[0050] The output factor information generation process (S3) is now complete. Once the output factor information generation process (S3) is complete, the selection process (S4) is executed.

[0051] 4-4. Selection process (S4) Next, as shown in Figure 6, the selection unit 3d performs the selection process (S4). Figure 8 shows a flowchart of the selection process. Once the selection process (S4) is performed, the selection unit 3d obtains the target machining accuracy from a set of basic input factor information (S41).

[0052] Next, the selection unit 3d calculates the change point at which the machining accuracy of the workpiece W changes (S42). For example, if runout is included as an example of the target machining accuracy of the workpiece W, the selection unit 3d calculates, for example, the 2 5 The predicted values ​​of the runout are calculated for the rough finishing division pattern of the street. Then, the rough finishing division pattern before and after the target runout is selected (S43).

[0053] Furthermore, if the cycle time is included as an example of the target machining accuracy of the workpiece W, the selection unit 3d will, for example, use the 2 mentioned above. 5 The predicted cycle time is calculated for the rough finishing division pattern of the street (S42). Then, the rough finishing division pattern is selected before and after the target cycle time (S43).

[0054] Furthermore, in the selection criteria for this configuration, the case in which rough finishing division is performed on all of the 1st to 5th cams 31 to 35 is selected as a reference example.

[0055] The selection process (S4) is now complete. Once the selection process (S4) is finished, the teaching process (S5) is executed.

[0056] 4-5. Instructional Processing (S5) Next, as shown in Figure 6, the teaching processing unit 3e performs the teaching process (S5). Specifically, the teaching processing unit 3e displays one set of basic output factor information and at least one set of teaching output factor information on the display device 5. As a result, the operator is taught one set of basic output factor information and at least one set of teaching output factor information.

[0057] Once the teaching process (S5) is complete, the operation of the machining support system 1 ends.

[0058] 5. Relationship between input and output factors during processing Referring to Figure 9, the relationship between input and output factors during various processes will be explained. In the input process (S1), the basic input factor information acquisition unit 3a acquires a set of basic input factor information. In Figure 9, the set of basic input factor information is represented by "In 1" enclosed in a rectangle.

[0059] Next, in the input factor information generation process (S2), the input factor information generation unit 3b generates multiple sets of variation input factor information from one set of basic input factor information. In Figure 9, the eight sets of variation input factor information are represented by the rectangles "In 2" to "In 9". The number of sets of variation input factor information is not limited to eight, but can be any number.

[0060] In the output factor information generation process (S3), the output factor information generation unit 3c generates a set of basic output factor information based on a set of basic input factor information. In Figure 9, the set of basic output factor information is represented by "Out 1" enclosed in a double-lined rectangle. The output factor information generation unit 3c generates multiple sets of corresponding variation output factor information based on multiple sets of variation input factor information. In Figure 9, eight sets of variation output factor information are represented by "Out 2" to "Out 9" enclosed in double-lined rectangles. The number of variation output factor information is the same as the number of variation input factor information.

[0061] In the selection process (S4), the selection unit 3d selects at least one set of instructional output factor information from multiple sets of variation output factor information. In Figure 9, the four sets of instructional output factor information are represented by double-lined rectangles labeled "Out 3," "Out 4," "Out 7," and "Out 8." The number of instructional factor information sets is not limited to four and can be any number.

[0062] In the teaching process (S5), the teaching processing unit 3e displays a set of basic output factor information and at least one set of teaching output factor information on the display device 5. In Figure 9, the set of basic output factor information represented by "Out 1" enclosed in a double-lined rectangle, and the four sets of teaching output factor information selected in the selection process (S4) ("Out 3", "Out 4", "Out 7", and "Out 8") are displayed on the display device 5.

[0063] The information shown in Figure 9 is a general explanation of the relationship between input and output factors during various processes, and therefore does not limit the explanation of the examples of input and output factor information described below.

[0064] 6. Examples of input factor information and examples of output factor information Next, we will explain examples of input factor information and output factor information related to this form. However, the content and types of input factor information and output factor information are not limited to the following explanation.

[0065] In this configuration, one set of basic input factor information includes the target cycle time and the target deviation. Other basic input factor information is omitted.

[0066] In this configuration, in the selection process (S4), the following four configurations are selected according to predetermined selection criteria. • When rough finishing division is performed on all of the 1st to 5th cams 31 to 35. • When rough finishing division is performed only on the second, third, and fourth cams 32, 33, and 34. • When rough finishing division is performed only on the third cam 33 • If rough finishing division is not performed on all of the 1st to 5th cams 31 to 35

[0067] When rough finishing division is performed on all of the first to fifth cams 31 to 35, the machining accuracy (e.g., runout) of the workpiece W improves, but the cycle time increases. Also, when machining the workpiece W with a grinding machine, the area near the center of the workpiece W in the Z-axis direction is prone to deflection due to the load applied during grinding. For this reason, by improving the machining accuracy of the third cam 33, which is fixed near the center in the Z-axis direction among the first to fifth cams 31 to 35, the overall machining accuracy of the workpiece W can be improved. For this reason, if rough finishing division is performed at only one location, it is preferable to perform it at the third cam 33. Furthermore, if rough finishing division is performed at multiple locations, it is preferable to perform it at the third cam 33, as well as at the second cam 32 and fourth cam 34 so as to be symmetrical with respect to the third cam 33.

[0068] Figure 10 shows how cycle time and runout change depending on the type of rough finishing division, with the horizontal axis representing cycle time and the vertical axis representing runout.

[0069] If rough finishing division is not performed on all of the first to fifth cams 31 to 35, the predicted runout of the workpiece W is approximately the same as the target runout, which is the target runout of the workpiece W after machining. The upper and lower limits of the predicted runout of the workpiece W are indicated by error bars. In other words, the predicted runout of the workpiece W includes the target runout between the upper and lower limits. On the other hand, the predicted cycle time is shorter than the target cycle time. In the prior art, since the upper limit of the predicted runout is greater than the target runout, the output factor information for cases where rough finishing division is not performed on all of the first to fifth cams 31 to 35 was not calculated at all. In this embodiment, if rough finishing division is not performed on all of the first to fifth cams 31 to 35, it corresponds to teaching output factor information.

[0070] When rough finishing division is performed only on the third cam 33, the predicted runout of the workpiece W is smaller than the target runout in both its upper and lower limits. On the other hand, the predicted cycle time is shorter than the target cycle time. In the prior art, since the predicted runout is smaller than the target runout and the predicted cycle time is shorter than the target cycle time, the case where rough finishing division is performed only on the third cam 33 was calculated and shown to the operator. In this embodiment, the case where rough finishing division is performed only on the third cam 33 corresponds to the basic output factor information.

[0071] When rough finishing division is performed on the second to fourth cams 34, the predicted values ​​of the runout of the workpiece W are smaller than the target runout at both the upper and lower limits. On the other hand, the predicted value of the cycle time is longer than the target cycle time. In the conventional technology, since the predicted value of the cycle time is longer than the target cycle time, the output factor information when rough finishing division is performed on the second to fourth cams 34 is not calculated at all. In this embodiment, when rough finishing division is performed on the second to fourth cams 34, it corresponds to teaching output factor information.

[0072] When rough finishing division is performed on all of the first to fifth cams 31 to 35, the predicted runout of the workpiece W is smaller than the target runout at both the upper and lower limits. On the other hand, the predicted cycle time is longer than the target cycle time. In the conventional technology, since the predicted cycle time is longer than the target cycle time, the output factor information when rough finishing division is performed on all of the first to fifth cams 31 to 35 is not calculated at all. In this embodiment, when rough finishing division is performed on all of the first to fifth cams 31 to 35, it corresponds to teaching output factor information.

[0073] 7. Effects of this embodiment In this configuration, a set of output factor information corresponding to a set of input factor information, and at least one set of teaching output factor information are displayed. This allows for the acquisition of multiple sets of output factor information by simply inputting one set of input factor information, even when multiple processing conditions are required or when customer requirements change, thus reducing the workload of inputting input factor information.

[0074] In this configuration, one set of basic input factor information includes the target machining accuracy, and the selection unit 3d selects at least one set of teaching output factor information from among multiple sets of variation output factor information based on the target machining accuracy.

[0075] By inputting a set of input factor information, including the target machining accuracy, it is possible to obtain teaching output factor information regarding the machining accuracy of the workpiece W. This eliminates the need to input multiple sets of input factor information and engage in trial and error to explore the machining accuracy of the workpiece W, thus saving labor in the input of input factor information.

[0076] Furthermore, in this embodiment, the selection unit 3d selects at least one set of teaching output factor information before and after the target machining accuracy.

[0077] By inputting a single set of input factor information, it is possible to obtain teaching output factor information before and after the target machining accuracy. This eliminates the need to input multiple sets of input factor information and engage in trial and error to explore the output factors before and after the machining accuracy of the workpiece W, thus saving labor in the input factor information process.

[0078] (Embodiment 2) Next, Embodiment 2 will be described with reference to Figures 11 and 12. In this embodiment, one set of basic input factor information includes the target roundness, which is the target value of the roundness of the workpiece W.

[0079] Furthermore, in this embodiment, the configuration of the selection process (S4) differs from that of Embodiment 1. Figure 11 shows a flowchart of the selection process (S4) according to this embodiment.

[0080] When the selection process (S4) is executed, the selection unit 3d obtains the target machining accuracy that will serve as the selection criterion from a set of basic input factor information (S44). In this embodiment, the arithmetic processing unit 3 obtains the target roundness.

[0081] Next, in S45, the selection unit 3d calculates predicted values ​​for the physical properties of the workpiece W. In this embodiment, the calculation processing unit 3 calculates, for example, the predicted value of roundness for the cycle time, increasing by 1 second from a lower limit (e.g., 20 seconds) to an upper limit (e.g., 10,000 seconds). However, the increase in cycle time may be any value of 2 seconds or more.

[0082] Next, the selection unit 3d calculates the predicted value change point where the predicted physical properties of the workpiece W change within the range from the lower limit to the upper limit of the cycle time (S45). In this embodiment, the predicted value change point where the predicted value of roundness changes abruptly is calculated.

[0083] Next, the selection unit 3d selects the cycle times corresponding to the points before and after the change in the predicted value of roundness (S46). In other words, it selects the cycle time before the predicted value of roundness changes abruptly and the cycle time after the predicted value of roundness changes abruptly.

[0084] Figure 12 shows a graph illustrating the change in roundness, with roundness on the vertical axis and cycle time on the horizontal axis. In this embodiment, roundness R1 and cycle time T1 before the rapid decrease in roundness, and roundness R2 and cycle time T2 after the rapid decrease in roundness are selected.

[0085] In addition, among the reference numerals used in Embodiment 2 and later, those that are the same as those used in the previously described embodiments represent the same components, etc., as those in the previously described embodiments, unless otherwise specified.

[0086] In this embodiment, the selection unit 3d selects at least one set of teaching output factor information from among multiple sets of variation output factor information, based on the predicted value change point where the predicted value of the machining accuracy of the workpiece W changes.

[0087] By inputting a single set of input factor information, it is possible to obtain teaching output factor information for the predicted change point where the predicted machining accuracy of the workpiece W changes. This eliminates the need to input multiple sets of input factor information and engage in trial and error to search for the change point where the machining accuracy of the workpiece W changes, thus saving labor in the input factor information process.

[0088] The selection unit 3d selects at least one set of teaching output factor information before and after the point of change in the predicted value.

[0089] By inputting a single set of input factor information, teaching output factor information can be obtained before and after the predicted value change point. This eliminates the need to input multiple sets of input factor information and engage in trial and error to search for output factor information before and after the change point where the machining accuracy of the workpiece W changes, thus saving labor in the input factor information process.

[0090] Each set of basic input factor information includes a target cycle time, while each set of variation input factor information includes a target cycle time different from that of the basic input factor information.

[0091] By inputting a single set of input factor information, multiple target cycle times can be obtained. Based on these multiple target cycle times, multiple cycle times can then be obtained as output factor information. As a result, there is no need to input multiple sets of input factor information and engage in trial and error to explore cycle times, thus streamlining the input process.

[0092] (Embodiment 3) Next, Embodiment 3 will be described. In this embodiment, the set of basic input factor information includes, for example, the factor information listed in Table 1.

[0093] [Table 1]

[0094] As shown in Table 1, a set of basic input factor information for this embodiment includes target cycle time (100 seconds), target roundness (2 μm), and target runout (20 μm).

[0095] The arithmetic processing unit 3, through teaching processing (S5), causes the display device 5 to display the basic output factor information shown in Table 2.

[0096] [Table 2]

[0097] As shown in Table 2, the basic output factor information includes cycle time prediction, roundness prediction, runout prediction, and interval prediction. The interval related to the interval prediction may be the truing interval or the dressing interval.

[0098] Each predicted value included in the basic output factor information is calculated to fit each target value included in the basic input factor information. Specifically, the predicted cycle time (99 seconds) is shorter than the target cycle time (100 seconds), the predicted roundness (1.0~1.5 μm) is smaller than the target roundness (2 μm), and the predicted runout (3~10 μm) is smaller than the target runout (20 μm). In addition, the interval predicted value of the basic output factor information for this configuration is set to 50.

[0099] Furthermore, the arithmetic processing unit 3 selects the teaching output factor information listed in Tables 3 to 7 from multiple sets of variation output factor information based on the selection criteria stored in the memory device 2 (S4), and displays the teaching output factor information on the display device 5 through teaching processing (S5). The teaching output factor information and selection criteria listed in each table are explained below.

[0100] [Table 3]

[0101] The teaching output factor information listed in Table 3 was selected based on the selection criterion of selecting variation factor information that includes interval prediction values ​​that are a predetermined amount larger than the interval prediction values ​​included in the basic output factor information related to Table 2. In this configuration, a set of variation output factor information containing 100 interval prediction values ​​was selected, which is 50 more than the interval prediction values ​​(50) related to the basic output factor information.

[0102] Comparing the basic output factor information listed in Table 2 with the teaching output factor information listed in Table 3, the predicted cycle time in Table 3 (109 seconds) is longer than the predicted cycle time in Table 2 (99 seconds).

[0103] [Table 4]

[0104] The teaching output factor information listed in Table 4 was selected based on the selection criteria of selecting variation output factor information that includes deflection prediction values ​​with improved accuracy by a predetermined amount compared to the deflection prediction values ​​included in the basic output factor information related to Table 2. In this configuration, a set of variation output factor information corresponding to the same 50 interval prediction values ​​as the 50 interval prediction values ​​related to the basic output factor information listed in Table 2 was selected.

[0105] Comparing the basic output factor information listed in Table 2 with the teaching output factor information listed in Table 4, the predicted cycle time in Table 4 (109 seconds) is longer than the predicted cycle time in Table 2 (99 seconds). Also, the predicted deflection value in Table 4 (0-3 μm) is smaller than the predicted deflection value in Table 2 (3-10 μm).

[0106] [Table 5]

[0107] The teaching output factor information listed in Table 5 was selected based on two criteria: selecting variation output factor information that includes deflection prediction values ​​with improved accuracy by a predetermined amount compared to the deflection prediction values ​​included in the basic output factor information related to Table 2; and selecting a set of variation output factor information corresponding to 100 interval prediction values, which is 50 more than the interval prediction values ​​(50) listed in Table 2.

[0108] Comparing the basic output factor information listed in Table 2 with the teaching output factor information listed in Table 5, the predicted cycle time in Table 5 (119 seconds) is longer than the predicted cycle time in Table 2 (99 seconds). Also, the predicted deflection value in Table 5 (0-3 μm) is smaller than the predicted deflection value in Table 2 (3-10 μm). Furthermore, the interval in Table 4 (100 cycles) is greater than the interval in Table 2 (50 cycles).

[0109] [Table 6]

[0110] The teaching output factor information listed in Table 6 was selected based on the selection criteria of selecting variation output factor information that includes cycle time prediction values ​​that are a predetermined amount shorter than the cycle time prediction values ​​included in the basic output factor information related to Table 2. In Table 6, a set of variation output factor information corresponding to a cycle time prediction value of 89 seconds, which is 10 seconds shorter than the cycle time prediction value (99 seconds) listed in Table 2, was selected.

[0111] Comparing the basic output factor information listed in Table 2 with the teaching output factor information listed in Table 6, the predicted cycle time in Table 6 (89 seconds) is shorter than the predicted cycle time in Table 2 (99 seconds).

[0112] [Table 7]

[0113] The teaching output factor information listed in Table 7 was selected based on the selection criteria of selecting variation output factor information that includes cycle time prediction values ​​that are a predetermined amount shorter than the cycle time prediction values ​​included in the basic output factor information related to Table 2. In Table 7, a set of variation output factor information corresponding to a cycle time prediction value of 79 seconds, which is 20 seconds shorter than the cycle time prediction value (99 seconds) listed in Table 2, was selected.

[0114] Comparing the basic output factor information listed in Table 2 with the teaching output factor information listed in Table 7, the predicted cycle time in Table 7 (79 seconds) is shorter than the predicted cycle time in Table 2 (99 seconds). Also, the predicted roundness in Table 7 (1.5~2.0 μm) is greater than the predicted roundness in Table 2 (1.0~1.5 μm). Furthermore, the predicted interval in Table 7 (30 pulses) is less than the predicted interval in Table 2 (50 pulses).

[0115] In this configuration, a set of output factor information corresponding to a set of input factor information, and at least one set of teaching output factor information are displayed. This allows for the acquisition of multiple sets of output factor information by simply inputting one set of input factor information, even when multiple processing conditions are required or when customer requirements change, thus reducing the workload of inputting input factor information.

[0116] (Embodiment 4) Next, Embodiment 4 will be described. In this embodiment, the operation of the input factor information generation process (S2) differs from that of Embodiment 1.

[0117] In the input factor information generation process (S2) according to this embodiment, the input factor information generation unit 3b acquires a set of basic input factor information. Next, the input factor information generation unit 3b acquires from the storage device 2 machine configuration information of other processing devices 6 that is different from the machine configuration information of the processing device 6 included in the set of basic input factor information.

[0118] Next, the input factor information generation unit 3b generates multiple sets of variation input factor information by replacing the machine configuration information of the processing device 6 included in one set of basic input factor information with the machine configuration information of other processing devices 6.

[0119] In this configuration, one set of basic input factor information includes machine configuration information for the processing device 6, and multiple sets of variation input factor information include machine configuration information for other processing machines that differ from one set of basic input factor information.

[0120] By inputting one set of input factor information, it is possible to obtain teaching output factor information for use with a different processing device 6 than the one entered. This eliminates the need to input multiple sets of input factor information and engage in trial and error to find output factor information when the processing device 6 is changed, thus saving labor in the input factor information input process.

[0121] (Embodiment 5) Next, Embodiment 5 will be described with reference to Figures 13 and 14. Figure 13 shows a flowchart of the processing support system 1 according to this embodiment.

[0122] As shown in Figure 13, when the processing support system 1 according to this embodiment is started, input processing (S1) is executed. In the input processing, a set of basic input factor information is input to the arithmetic processing unit 3 from the operator via an input device 4 such as a keyboard.

[0123] Next, the machine learning process (S6) is executed. As shown in Figure 14, when the machine learning process (S6) is executed, the arithmetic processing unit 3 acquires a set of basic input factor information. Next, the arithmetic processing unit 3 acquires from the storage device 2 the selection criteria used when the arithmetic processing unit 3 selects teaching output factor information from the variation output factor information in the selection process (S4).

[0124] Next, the processing unit 3 performs machine learning using a set of basic input factor information as training data. This updates the selection criteria for at least one set of teaching output factor information in the knowledge model M. The processing unit 3 stores the updated selection criteria in the storage device 2. This completes the machine learning process (S6).

[0125] As shown in Figure 13, once the machine learning process (S6) is completed, the input factor information generation process (S2) is executed. The subsequent processes are the same as in Embodiment 1, so their explanation is omitted.

[0126] In this configuration, the selection criteria for at least one set of instructional output factor information in the knowledge model M are updated by machine learning, using a set of basic input factor information as training data.

[0127] The set of basic input factor information input into the machining support system 1 is tailored to the user's requirements. Therefore, as the system learns the set of basic input factor information requested by the user, the selection criteria for at least one set of teaching output factors in the knowledge model M are updated, allowing for the acquisition of teaching output factors that are more accurately adapted to the user's requirements.

[0128] The present invention is not limited to the embodiments described above, and can be applied to various embodiments without departing from its spirit.

[0129] When selecting teaching output factor information based on the change point where the predicted machining accuracy of the workpiece W changes, the type of machining accuracy of the workpiece W can be any type, such as roundness, runout, or surface roughness.

[0130] The system may also be configured to teach a predicted CP value, selected based on the CP value of the workpiece W, as teaching output factor information. [Explanation of Symbols]

[0131] 1 Machining support system, 2 Storage device, 3 Arithmetic processing unit, 3a Basic input factor information acquisition unit, 3b Input factor information generation unit, 3c Output factor information generation unit, 3d Selection unit, 3e Teaching processing unit, 6 Machining device, M Knowledge model, W Workpiece

Claims

1. A machining support system that utilizes a knowledge model that defines the relationships between factors in the machining of a workpiece by a machining device, A storage device for storing the aforementioned knowledge model, A processing unit that performs calculations using the aforementioned knowledge model, Equipped with, The aforementioned arithmetic processing unit is A basic input factor information acquisition unit that acquires a set of basic input factor information, An input factor information generation unit generates multiple sets of variation input factor information that are different from the set of basic input factor information based on the set of basic input factor information, An output factor information generation unit generates, based on the knowledge model, a set of basic output factor information corresponding to the set of basic input factor information, and a set of variation output factor information corresponding to the set of variation input factor information. A selection unit that selects at least one set of teaching output factor information from the aforementioned multiple sets of variation output factor information, A teaching processing unit that teaches the aforementioned set of basic output factor information and teaches at least the selected set of teaching output factor information, Equipped with, The aforementioned set of basic input factor information includes the target machining accuracy, The selection unit is a machining support system that selects at least one set of teaching output factor information before and after the target machining accuracy from among the multiple sets of variation output factor information, based on the target machining accuracy.

2. A machining support system that utilizes a knowledge model that defines the relationships between factors in the machining of a workpiece by a machining device, A storage device for storing the aforementioned knowledge model, A processing unit that performs calculations using the aforementioned knowledge model, Equipped with, The aforementioned arithmetic processing unit is A basic input factor information acquisition unit that acquires a set of basic input factor information, An input factor information generation unit generates multiple sets of variation input factor information that are different from the set of basic input factor information based on the set of basic input factor information, An output factor information generation unit generates, based on the knowledge model, a set of basic output factor information corresponding to the set of basic input factor information, and a set of variation output factor information corresponding to the set of variation input factor information. A selection unit that selects at least one set of teaching output factor information from the aforementioned multiple sets of variation output factor information, A teaching processing unit that teaches the aforementioned set of basic output factor information and teaches at least the selected set of teaching output factor information, Equipped with, The selection unit is a machining support system that selects at least one set of teaching output factor information before and after a predicted value change point from among the multiple sets of variation output factor information, based on the predicted value change point where the predicted value of the machining accuracy of the workpiece changes.

3. The aforementioned set of basic input factor information includes the target cycle time, The machining support system according to claim 1 or 2, wherein the multiple sets of variation input factor information include a target cycle time different from that of the single set of basic input factor information.

4. The aforementioned set of basic input factor information includes the mechanical configuration information of the processing apparatus, The machining support system according to claim 1 or 2, wherein the multiple sets of variation input factor information include machine configuration information of other machining equipment that is different from the one set of basic input factor information.

5. The processing support system according to claim 1 or 2, wherein the selection criteria for at least one set of teaching output factor information in the knowledge model are updated by machine learning using the input set of basic input factor information as training data.

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