Manufacturing method of workpiece, machining method of workpiece, and machining condition derivation device

Abstract

To properly determine a machining condition used for actual machining of a workpiece in a short time.SOLUTION: A method for manufacturing a workpiece includes the steps of: using an experimental design method in CAE to determine a significant factor among a plurality of factors in machining of the workpiece in a state where the validity of the CAE is verified; deriving a machining condition in which one level is selected from a plurality of levels of the significant factor; performing the actual machining using the machining condition and confirming the appropriateness of the machining condition; and manufacturing the workpiece using the machining condition in which the suitability is confirmed.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a method for manufacturing a workpiece, a method for machining a workpiece, and a machining condition deriving device. [Background technology]

[0002] Patent Document 1 describes a characteristic analysis method for analyzing predetermined physical characteristics of a product. This characteristic analysis method includes a step of performing a numerical simulation to analyze the physical characteristics of the product based on parameters including attribute values ​​obtained by measuring a prototype of the product, and the parameters include, in addition to the attribute values, predetermined parameters that are modified based on the results of a comparison between predetermined experimental results of the prototype and the results of a numerical simulation of the experiment.

[0003] Patent document 2 describes a method for identifying locations that cause deviations in springback amount, which includes the steps of acquiring a driving stress distribution of a press-formed product, acquiring a driving stress distribution from a springback analysis, setting a stress difference distribution between the analytical driving stress distribution and the formed product driving stress distribution to the formed product shape at bottom dead center, acquiring a springback amount based on the stress difference distribution, changing the value of a portion of the stress difference distribution to acquire the springback amount, and comparing the acquired springback amounts to identify locations that cause deviations in the springback amount.

[0004] Patent Document 3 describes a CAE analysis algorithm verification method including the steps of: measuring, using a three-dimensional measuring device, the positions of actual analysis points, which are one or more points including measurement points located on the surface of an actual object to be measured; measuring actual measurement values, which are measurement values ​​of specified parameters at the actual measurement points when a load is applied to the actual object to be measured; setting virtual analysis points at positions equivalent to the actual analysis points in CAD data of the object to be measured; creating mesh model data based on the CAD data so that mesh nodes are located at the virtual analysis points; and calculating virtual measurement values, which are the values ​​of specified parameters at the virtual measurement points when a load is virtually applied to the mesh model, based on an analysis algorithm, and verifying the analysis algorithm based on the obtained actual measurement values ​​and virtual measurement values. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2004-171144 A [Patent Document 2] JP 2020-179409 A [Patent Document 3] JP 2005-182529 A Summary of the Invention [Problem to be solved by the invention]

[0006] The processing conditions used in the actual processing of workpieces are generally determined by the engineer's experience, intuition, tips, past cases, etc. However, with this method of determination, it is difficult to obtain certainty when setting the processing conditions required for prototyping or quotations, and it is difficult to determine the processing conditions in a short period of time. In addition, since the processing conditions are not backed by technical evidence, it is also difficult to determine the processing conditions appropriately.

[0007] An object of the present invention is to appropriately determine, in a short period of time, machining conditions to be used in the actual machining of a workpiece. [Means for solving the problem]

[0008] With this objective in mind, the present invention provides a method for manufacturing a workpiece, including the steps of verifying the validity of CAE for actual machining of a workpiece, determining a significant factor among multiple factors in the machining of the workpiece using an experimental design method in CAE when the validity of the CAE has been verified, deriving machining conditions by selecting one level from multiple levels of the significant factor, performing actual machining using the machining conditions to confirm the appropriateness of the machining conditions, and manufacturing the workpiece using the machining conditions whose appropriateness has been confirmed.

[0009] In the verifying step, a first processing condition may be identified among all processing conditions in which one level is selected from multiple levels of each of a plurality of factors for each of the plurality of factors, and the validity of the result of a simulation by CAE using the first processing condition may be verified against the result of actual processing using the first processing condition.

[0010] In this case, in the determining step, the significant factors may be determined based on the result of a simulation by CAE using second processing conditions in which one level is selected from a part of the multiple levels of each of the multiple factors. Also, in the determining step, the significant factors may be determined further based on the result of a simulation by CAE using the first processing conditions in the verifying step.

[0011] In this case, in the deriving step, one level may be selected from multiple levels of a significant factor based on the relationship between the results of actual machining using the first machining conditions in the verifying step and the results of a simulation by CAE.

[0012] The present invention also provides a method for manufacturing a processed product, in which, in the determining step, at least two factors are determined as significant factors, and in the deriving step, an experimental design method is used in CAE to determine the presence or absence of an interaction between the at least two factors, and processing conditions are derived for each of the at least two factors, in which one level is selected from multiple levels of the factor, using the presence or absence of the interaction.

[0013] In the verifying step, a first processing condition may be identified among all processing conditions in which one level is selected from multiple levels of each of a plurality of factors for each of the plurality of factors, and the validity of the result of a simulation by CAE using the first processing condition may be verified against the result of actual processing using the first processing condition.

[0014] In this case, in the determining step, the significant factors may be determined based on the results of a simulation by CAE using second processing conditions in which one level is selected from a part of the multiple levels of each of the multiple factors. Also, in the deriving step, the presence or absence of an interaction may be determined based on the results of a simulation by CAE using third processing conditions in which one factor is selected from all of the multiple levels of each of at least two factors. Furthermore, in the deriving step, the presence or absence of an interaction may be determined further based on the results of the simulation by CAE using the first processing conditions in the verifying step and the results of the simulation by CAE using the second processing conditions when determining the significant factors.

[0015] In this case, in the deriving step, one level may be selected from a plurality of levels of the factor for each of at least two factors based on the relationship between the result of actual machining using the first machining condition in the verifying step and the result of a simulation by CAE.

[0016] Furthermore, the present invention also provides a method for processing a workpiece, including the steps of verifying the validity of CAE for actual processing of a workpiece, determining a significant factor among multiple factors in processing of the workpiece using an experimental design method in CAE when the validity of the CAE has been verified, deriving processing conditions by selecting one level from multiple levels of the significant factor, and performing actual processing using the processing conditions to confirm the appropriateness of the processing conditions.

[0017] Furthermore, the present invention also provides a machining condition derivation device comprising: a verification means for verifying the validity of CAE for actual machining of a workpiece; a determination means for determining a significant factor among a plurality of factors in the machining of the workpiece by using an experimental design method in the CAE when the validity of the CAE has been verified; and a derivation means for deriving a machining condition by selecting one level from a plurality of levels of the significant factor. Effect of the Invention

[0018] According to the present invention, the machining conditions to be used for the actual machining of the workpiece can be appropriately determined in a short time. [Brief description of the drawings]

[0019] [Figure 1] 1 is a flowchart showing a flow of a machining condition verification method according to an embodiment of the present invention. [Diagram 2] FIG. 13 is a diagram showing the results of actual measurement by actual processing in step 1. [Diagram 3] FIG. 1 shows the analysis results by CAE in step 1. [Figure 4-1] Graph (a) is a graph showing the change in the measured and analytical values ​​of the principal component of force when the cutting speed value is changed, and graph (b) is a graph showing the relationship between the measured and analytical values ​​of the principal component of force. [Figure 4-2] Graph (a) is a graph showing the change in the measured and analytical values ​​of the feed force when the cutting speed value is changed, and graph (b) is a graph showing the relationship between the measured and analytical values ​​of the feed force. [Figure 4-3] Graph (a) is a graph showing the change in the measured and analyzed values ​​of machining efficiency when the cutting speed value is changed, and graph (b) is a graph showing the relationship between the measured and analyzed values ​​of machining efficiency. [Figure 4-4] Graph (a) is a graph showing the change in the measured and analyzed values ​​of surface roughness when the cutting speed value is changed, and graph (b) is a graph showing the relationship between the measured and analyzed values ​​of surface roughness. [Figure 5-1]Graph (a) is a graph showing the change in the actual measured value and analytical value of the principal component force when the cutting depth value is changed, and graph (b) is a graph showing the relationship between the actual measured value and analytical value of the principal component force. [Figure 5-2] Graph (a) is a graph showing the change in the measured and analytical values ​​of the feed force when the cutting depth value is changed, and graph (b) is a graph showing the relationship between the measured and analytical values ​​of the feed force. [Figure 5-3] Graph (a) is a graph showing the change in the actual measured value and the analytical value of the machining efficiency when the cutting depth value is changed, and graph (b) is a graph showing the relationship between the actual measured value and the analytical value of the machining efficiency. [Figure 5-4] Graph (a) is a graph showing the change in the measured and analyzed values ​​of surface roughness when the cutting depth value is changed, and graph (b) is a graph showing the relationship between the measured and analyzed values ​​of surface roughness. [Figure 6-1] Graph (a) is a graph showing the change in the actual measured value and analytical value of the principal component force when the feed value is changed, and graph (b) is a graph showing the relationship between the actual measured value and analytical value of the principal component force. [Figure 6-2] Graph (a) is a graph showing the change in the measured and analytical values ​​of the feed force when the feed value is changed, and graph (b) is a graph showing the relationship between the measured and analytical values ​​of the feed force. [Figure 6-3] Graph (a) is a graph showing the change in the measured and analyzed values ​​of machining efficiency when the feed value is changed, and graph (b) is a graph showing the relationship between the measured and analyzed values ​​of machining efficiency. [Figure 6-4] Graph (a) shows the change in the measured and analyzed values ​​of surface roughness when the feed value is changed, and graph (b) shows the relationship between the measured and analyzed values ​​of surface roughness. [Figure 7] FIG. 13 shows an orthogonal table used in steps 2 and 3. [Figure 8] FIG. 13 shows the analysis results by CAE in step 2. [Figure 9-1] 13(a) and 13(b) are diagrams showing a method for determining significant factors regarding the principal force based on the analysis results by CAE in step 2. [Figure 9-2]11(a) to 11(c) are diagrams showing a method for determining significant factors regarding the transmission force based on the analysis results by CAE in step 2. [Figure 9-3] FIG. 13 is a diagram showing a method for determining significant factors regarding processing efficiency based on the analysis results by CAE in step 2. [Figure 9-4] FIG. 13 is a diagram showing a method for determining significant factors regarding surface roughness based on the analysis results by CAE in step 2. [Figure 10] FIG. 9-1 to FIG. 9-4 are summarized. [Figure 11] FIG. 13 shows the analysis results by CAE in step 3. [Figure 12-1] 13(a) and 13(b) are diagrams showing a method for determining whether there is a significant interaction between factors regarding the principal force based on the analysis results by CAE in step 3. [Figure 12-2] 11(a) to 11(c) are diagrams showing a method for determining whether or not there is a significant interaction between factors regarding the transmission force, based on the analysis results by CAE in step 3. [Figure 12-3] 13(a) and 13(b) are diagrams showing a method for determining whether there is a significant interaction between factors regarding processing efficiency based on the analysis results by CAE in step 3. [Figure 12-4] FIG. 13 is a diagram showing a method for determining whether or not there is a significant interaction between factors regarding surface roughness based on the analysis results by CAE in step 3. [Figure 13] FIG. 12 is a diagram summarizing the results of FIGS. 12-1 to 12-4. [Figure 14] FIG. 13 is a diagram showing a method for determining optimal processing conditions for each factor for each evaluation item. [Figure 15] 1 is a block diagram showing an example of a functional configuration of a machining condition deriving device according to an embodiment of the present invention; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0021] [Outline of processing condition verification method] FIG. 1 is a flowchart showing the flow of the processing condition verification method according to the present embodiment.

[0022] As shown in the figure, in the machining condition verification method of this embodiment, first, the validity of CAE for actual machining of a workpiece is verified (step 1). Next, in a state where the validity of CAE has been verified, significant factors among a plurality of factors in machining of a workpiece are determined in CAE using an experimental design method (step 2). Next, in CAE, the presence or absence of interactions between significant factors is determined using an experimental design method, and machining conditions in which one level is selected from a plurality of levels of significant factors are derived using the significant factors and the presence or absence of interactions between the significant factors (step 3). Finally, actual machining is performed using the machining conditions, and the appropriateness of the machining conditions is confirmed (step 4).

[0023] Each step will be explained in detail below. In this example, cutting speed V [m / min], cutting depth ap [mm], and feed F [mm / rev] are used as examples of factors in machining the workpiece. Seven evaluation items are used as evaluation items in machining the workpiece. These seven evaluation items are principal force [N], thrust force [N], and feed force [N] for the machining load, machining efficiency [mm 3 / sec] for product quality, surface roughness [μm], product bending [μm] for tool life, and tool temperature [℃].

[0024] [Step 1 Contents] As described above, step 1 is a step for verifying the validity of CAE for actual machining of a workpiece. Specifically, in step 1, actual machining using an actual machine and analysis using CAE are performed, and the actual machining and CAE are linked by evaluating the relationship between the actual measurement results of the actual machining and the analysis results by CAE. Note that the linking of the actual machining and CAE does not necessarily have to be performed in a state where the actual measurement results of the actual machining and the analysis results by CAE match. For example, it is sufficient to perform the linking in a state where some relationship is maintained between the actual measurement results of the actual machining and the analysis results by CAE, such as the actual measurement results of the actual machining being always several times larger than the analysis results by CAE.

[0025] FIG. 2 shows the results of measurements taken during actual processing in step 1.

[0026] In FIG. 2, the standard levels of the factors of the cutting speed V, the cutting depth ap, and the feed rate F are set to 120, 0.175, and 0.175, respectively, and smaller and larger levels are set. Specifically, for the cutting speed V, the small level is 80, the large level is 160, and the extremely small level added as an exception is 40. For the cutting depth ap, the small level is 0.07, and the large level is 0.35. For the feed rate F, the small level is 0.07, and the large level is 0.42. Conditions 1 to 8 are configured by selecting one level from multiple levels for each of the factors of the cutting speed V, the cutting depth ap, and the feed rate F. Here, conditions 1 to 8 are an example of a first machining condition among all machining conditions in which one level is selected from multiple levels for each of multiple factors.

[0027] In addition, in Fig. 2, two actual measured values ​​were obtained for each evaluation item of the main force, thrust force, feed force, surface roughness, product bending, and tool temperature, and these are shown in the rows of types n1 and n2, respectively, and their average value is shown in the row of type ave. For the machining efficiency, one actual measured value was obtained, and this is shown across types n1, n2, and ave.

[0028] FIG. 3 shows the analysis results by CAE in step 1.

[0029] Conditions 1 to 8 are the same as those described with reference to Figure 2, so their explanation will be omitted. In Figure 3, one analytical value is obtained for each evaluation item of the main force, thrust force, feed force, machining efficiency, surface roughness, product bending, and tool temperature, and these are shown in the row of the type ave.

[0030] Figures 4-1 to 4-4, 5-1 to 5-4, and 6-1 to 6-4 are graphs for evaluating the relationship between the actual measurement results of actual machining and the analysis results by CAE. In this embodiment, seven evaluation items are used, but only the main force, feed force, machining efficiency, and surface roughness are shown here. The thrust force, product bending, and tool temperature are not shown.

[0031] (a) of each figure includes an actual value graph showing the change in the actual value of the evaluation item when the value of the factor is changed, and an analytical value graph showing the change in the analytical value of the evaluation item when the value of the factor is changed. The actual value graph is shown by a solid line in FIG. 2, where points showing the actual value of the evaluation item when the level of the factor is changed while the levels of other factors other than the factor are fixed at the reference level are plotted, and the points are connected by straight lines. The analytical value graph is shown by a dashed line in FIG. 3, where points showing the analytical value of the evaluation item when the level of the factor is changed while the levels of other factors other than the factor are fixed at the reference level are plotted, and the points are connected by straight lines.

[0032] In addition, (b) of each figure shows the points corresponding to the combination of the actual values ​​in the actual value graph in (a) and the analytical values ​​in the analytical value graph for each level of the factor, plotted, and a regression line drawn between these points. (b) shows the equation of this regression line and the coefficient of determination R, which indicates the goodness of fit of the regression line. 2 Also, in (b), symbols are shown to indicate the evaluation of the relationship between the measured values ​​and the analytical values. The symbol ◎ indicates the coefficient of determination R 2 Regarding "R 2>0.7", there is no outlying data, and there is no negative correlation (the a in the regression line equation y=ax+b is not negative). The symbol ○ indicates that the points remain stable and do not change even when the factor values ​​are changed. In this case, there is no correlation, but it will be treated as an evaluation of the symbol ○. The symbol △ indicates that the values ​​are so small that the change is hidden or unclear. The symbol × indicates that even one of the conditions of the symbols ◎ and ○ is not met.

[0033] FIG. 4-1(a) is a graph showing the change in the measured and analytical values ​​of the principal force when the cutting speed V is changed, and FIG. 4-1(b) is a graph showing the relationship between the measured and analytical values ​​of the principal force. In this case, R 2 =0.7807>0.7, and there is no outlier, but since it is a negative correlation, the symbol ◎ is not used. On the other hand, even if the cutting speed V is changed, the point in Figure 4-1(b) remains stable and is therefore marked with a ○ symbol.

[0034] Also, a graph showing the change in the actual measured value and analytical value of the thrust force when the cutting speed V is changed is similar to Fig. 4-1(a) although not shown, and a graph showing the relationship between the actual measured value and analytical value of the thrust force is similar to Fig. 4-1(b) although not shown. Therefore, the graph showing the relationship between the actual measured value and analytical value of the thrust force is marked with the symbol ○ as in Fig. 4-1(b).

[0035] FIG. 4-2(a) is a graph showing the change in the measured and analytical values ​​of the feed force when the cutting speed V is changed, and FIG. 4-2(b) is a graph showing the relationship between the measured and analytical values ​​of the feed force. In this case, R 2 =0.9034>0.7, and there is no outlier data, but since it is a negative correlation, the symbol ◎ is not used. On the other hand, even if the value of cutting speed V is changed, the point in Figure 4-2(b) remains stable and is therefore marked with a symbol ○. In addition, since the value is so small that the change is hidden or unclear, the symbol △ is also used.

[0036] FIG. 4-3(a) is a graph showing the change in the measured and analytical values ​​of the machining efficiency when the cutting speed V is changed, and FIG. 4-3(b) is a graph showing the relationship between the measured and analytical values ​​of the machining efficiency. In this case, R 2 =0.9702>0.7, there is no outlying data, and it is not a negative correlation, so the symbol ◎ is used.

[0037] Figure 4-4(a) is a graph showing the change in the measured and analyzed values ​​of surface roughness when the cutting speed V is changed, and Figure 4-4(b) is a graph showing the relationship between the measured and analyzed values ​​of surface roughness. In this case, there is no outlier data and no negative correlation, but R 2 is not found, and R 2 > 0.7, so the symbol ◎ is not given. On the other hand, even if the cutting speed V is changed, the point in Figure 4-4(b) remains stable and is given the symbol ○.

[0038] Also, a graph showing the change in the actual measured value and analytical value of the product bending when the value of the cutting speed V is changed is not shown, but is similar to Figure 4-1(a), and a graph showing the relationship between the actual measured value and analytical value of the product bending is not shown, but is similar to Figure 4-1(b). Therefore, the graph showing the relationship between the actual measured value and analytical value of the product bending is marked with the symbol ○, just like Figure 4-1(b).

[0039] Furthermore, a graph showing the change in the actual and analytical values ​​of the tool temperature when the value of the cutting speed V is changed is similar to Figure 4-3(a) (not shown), and a graph showing the relationship between the actual and analytical values ​​of the tool temperature is similar to Figure 4-3(b) (not shown). Therefore, the graph showing the relationship between the actual and analytical values ​​of the tool temperature is marked with the symbol ◎, as in Figure 4-3(b).

[0040] FIG. 5-1(a) is a graph showing the change in the actual measured value and analytical value of the principal component force when the cutting depth ap is changed, and FIG. 5-1(b) is a graph showing the relationship between the actual measured value and analytical value of the principal component force. In this case, R 2=0.9981>0.7, there is no outlying data, and it is not a negative correlation, so the symbol ◎ is used.

[0041] Also, a graph showing the change in the actual and analytical values ​​of the thrust force when the cutting depth ap is changed is not shown, but is similar to Figure 5-1(a), and a graph showing the relationship between the actual and analytical values ​​of the thrust force is not shown, but is similar to Figure 5-1(b). Therefore, the graph showing the relationship between the actual and analytical values ​​of the thrust force is marked with the symbol ◎, as in Figure 5-1(b).

[0042] FIG. 5-2(a) is a graph showing the change in the measured value and the analytical value of the feed force when the value of the cutting depth ap is changed, and FIG. 5-2(b) is a graph showing the relationship between the measured value and the analytical value of the feed force. In this case, R 2 =0.9714>0.7, there is no outlying data, and it is not a negative correlation, so the symbol ◎ is used.

[0043] FIG. 5-3(a) is a graph showing the change in the measured value and the analytical value of the machining efficiency when the value of the cutting depth ap is changed, and FIG. 5-3(b) is a graph showing the relationship between the measured value and the analytical value of the machining efficiency. In this case, R 2 Since =1>0.7, there is no outlying data, and it is not a negative correlation, the symbol ◎ is used.

[0044] Figure 5-4(a) is a graph showing the change in the measured and analytical values ​​of surface roughness when the cutting depth ap is changed, and Figure 5-4(b) is a graph showing the relationship between the measured and analytical values ​​of surface roughness. In this case, there is no outlier data and no negative correlation, but R 2 = 0, R 2 > 0.7, so the symbol ◎ is not used. On the other hand, even if the cutting depth ap value is changed, the point in Figure 5-4(b) remains stable and is marked with a ○ symbol.

[0045] Also, a graph showing the change in the actual measured value and analytical value of the product bending when the value of the cutting depth ap is changed is not shown, but is similar to Figure 5-1(a), and a graph showing the relationship between the actual measured value and analytical value of the product bending is not shown, but is similar to Figure 5-1(b). Therefore, the graph showing the relationship between the actual measured value and analytical value of the product bending is marked with the symbol ◎, just like Figure 5-1(b).

[0046] Furthermore, a graph showing the change in the actual and analytical values ​​of the tool temperature when the value of the cutting depth ap is changed is similar to Fig. 5-3(a) (not shown), and a graph showing the relationship between the actual and analytical values ​​of the tool temperature is similar to Fig. 5-3(b) (not shown). Therefore, the graph showing the relationship between the actual and analytical values ​​of the tool temperature is marked with the symbol ◎, as in Fig. 5-3(b).

[0047] FIG. 6-1(a) is a graph showing the change in the actual measured value and analytical value of the principal component force when the value of the feed F is changed, and FIG. 6-1(b) is a graph showing the relationship between the actual measured value and analytical value of the principal component force. In this case, R 2 =0.9965>0.7, there is no outlying data, and it is not a negative correlation, so the symbol ◎ is used.

[0048] Also, a graph showing the change in the actual measured value and analytical value of the thrust force when the feed F value is changed is not shown, but is similar to Figure 6-1(a), and a graph showing the relationship between the actual measured value and analytical value of the thrust force is not shown, but is similar to Figure 6-1(b). Therefore, the graph showing the relationship between the actual measured value and analytical value of the thrust force is marked with the symbol ◎, just like Figure 6-1(b).

[0049] Figure 6-2(a) is a graph showing the change in the measured and analytical values ​​of the feed force when the value of the feed F is changed, and Figure 6-2(b) is a graph showing the relationship between the measured and analytical values ​​of the feed force. In this case, there is no outlier data and no negative correlation, but R 2 = 0.2477, R 2> 0.7, so the symbol ◎ is not used. On the other hand, even if the feed F value is changed, the point in Figure 6-2(b) remains stable and is therefore marked with a symbol ○. In addition, the value is so small that the change is hidden or unclear, so the symbol △ is also used.

[0050] FIG. 6-3(a) is a graph showing the change in the measured and analytical values ​​of the machining efficiency when the feed rate F is changed, and FIG. 6-3(b) is a graph showing the relationship between the measured and analytical values ​​of the machining efficiency. In this case, R 2 =0.9568>0.7, there is no outlying data, and it is not a negative correlation, so the symbol ◎ is used.

[0051] FIG. 6-4(a) is a graph showing the change in the measured surface roughness and the analytical value when the feed rate F is changed, and FIG. 6-4(b) is a graph showing the relationship between the measured surface roughness and the analytical value. In this case, R 2 =0.9967>0.7, there is no outlying data, and it is not a negative correlation, so the symbol ◎ is used.

[0052] Also, a graph showing the change in the actual and analytical values ​​of the product bending when the feed F value is changed is not shown, but is similar to Figure 6-1(a), and a graph showing the relationship between the actual and analytical values ​​of the product bending is not shown, but is similar to Figure 6-1(b). Therefore, the graph showing the relationship between the actual and analytical values ​​of the product bending is marked with the symbol ◎, just like Figure 6-1(b).

[0053] Furthermore, a graph showing the change in the actual and analytical values ​​of the tool temperature when the feed F value is changed is similar to Figure 6-3(a) (not shown), and a graph showing the relationship between the actual and analytical values ​​of the tool temperature is similar to Figure 6-3(b) (not shown). Therefore, the graph showing the relationship between the actual and analytical values ​​of the tool temperature is marked with the symbol ◎, as in Figure 6-3(b).

[0054] [Step 2 Contents] As described above, step 2 is a step in which, after the validity of CAE has been verified, significant factors among multiple factors in the processing of the workpiece are determined in the CAE using an experimental design method.

[0055] FIG. 7 is a diagram showing an orthogonal array used in step 2. An orthogonal array is a table in which a plurality of levels are assigned to each of a plurality of factors. In this embodiment, the cutting speed V, the cutting depth ap, and the feed F are taken as examples of a plurality of factors, and these are set in the rows of the orthogonal array. A small level (hereinafter referred to as a "small level"), a large level (hereinafter referred to as a "large level"), and an intermediate level (hereinafter referred to as a "medium level") are assigned to each factor. Specifically, 80 is assigned as the small level to the cutting speed V, 160 is assigned as the large level, and 120 is assigned as the medium level. 0.07 is assigned as the small level to the cutting depth ap, 0.35 is assigned as the large level, and 0.175 is assigned as the medium level. 0.07 is assigned as the small level to the cutting depth ap, 0.42 is assigned as the large level, and 0.175 is assigned as the medium level to the feed F.

[0056] FIG. 8 shows the analysis results by CAE in step 2.

[0057] In Figure 8, the conditions are configured by selecting either a small level or a medium level for each factor of cutting speed V, cutting depth ap, and feed F. Specifically, for cutting speed V, either a small level of 80 or a medium level of 120 is selected. For cutting depth ap, either a small level of 0.07 or a medium level of 0.175 is selected. For feed F, either a small level of 0.07 or a medium level of 0.175 is selected.

[0058] Incidentally, the conditions newly configured in step 2 are only conditions 9 to 12 enclosed in a thick frame in the figure. Conditions 1, 3, 5, and 7 have already been configured in step 1. Therefore, the analysis values ​​of the evaluation items of the main force, thrust force, feed force, machining efficiency, surface roughness, product bending, and tool temperature for conditions 1, 3, 5, and 7 may be those shown in FIG. 3. That is, only the analysis values ​​of the evaluation items of the main force, thrust force, feed force, machining efficiency, surface roughness, product bending, and tool temperature for conditions 9 to 12 may be set in FIG. 8. Here, conditions 1, 3, 5, and 7 are examples of the first machining conditions described in step 1. Conditions 9 to 12 are examples of second machining conditions in which one level is selected from a part of the multiple levels of a factor for each of multiple factors.

[0059] Alternatively, the analytical values ​​of the evaluation items of the main force, thrust force, feed force, machining efficiency, surface roughness, product bending, and tool temperature for condition 1, condition 3, condition 5, and condition 7 do not have to be those shown in Fig. 3. In this case, condition 1, condition 3, condition 5, condition 7, and conditions 9 to 12 are examples of second machining conditions in which one level is selected from a portion of multiple levels of each of multiple factors.

[0060] 9-1 to 9-4 are diagrams showing a method for determining significant factors for each evaluation item based on the analysis results by CAE in step 2. In this embodiment, seven evaluation items are used, but here too, only the main force, feed force, machining efficiency, and surface roughness are shown. The thrust force, product bending, and tool temperature are not shown.

[0061] (a) of each figure (whole figure if not present) shows the results of an analysis of variance to remove one insignificant factor from three factors. (b) of each figure (if present) shows the results of an analysis of variance to remove one insignificant factor from the two factors remaining in (a). (c) of each figure (if present) shows the results of an analysis of variance to verify whether the one factor remaining in (b) is significant.

[0062] In the figures, "**" indicates 1% significance. 1% significance means that the P value, expressed as a percentage, is within 1%. Additionally, "*" indicates 5% significance. 5% significance means that the P value, expressed as a percentage, is within 5%. In what follows, a P value of 5% or less can be considered significant, so both 1% significance and 5% significance will be considered "significant."

[0063] Figure 9-1(a) shows the results of an analysis of variance to exclude one insignificant factor from cutting speed V, cutting depth ap, and feed rate F when principal force is used as an evaluation item. Here, cutting speed V is excluded because its test result is not significant and its P value (upper probability) is the largest.

[0064] FIG. 9-1(b) shows the results of an analysis of variance to exclude one insignificant factor from the cutting depth ap and feed F after excluding the cutting speed V in FIG. 9-1(a). Here, since the P values ​​(upper probability) of the cutting depth ap and feed F are not higher than in FIG. 9-1(a), they are evaluated with the cutting speed V excluded. And, since the test results of both the cutting depth ap and feed F are significant, they are not excluded. Therefore, the cutting depth ap and feed F become significant factors.

[0065] In addition, when thrust force is used as an evaluation item, the results of an analysis of variance to exclude one insignificant factor from cutting speed V, cutting depth ap, and feed F are not shown, but tend to be similar to Figures 9-1(a) and (b). That is, cutting speed V is excluded, but cutting depth ap and feed F are not excluded. Therefore, cutting depth ap and feed F become significant factors.

[0066] Figure 9-2(a) shows the results of an analysis of variance to exclude one insignificant factor from cutting speed V, cutting depth ap, and feed F when feed force is used as the evaluation item. Here, cutting speed V is excluded because its test result is not significant and its P value (upper probability) is the largest.

[0067] FIG. 9-2(b) shows the results of an analysis of variance to exclude one insignificant factor from the cutting depth ap and feed F after excluding the cutting speed V in FIG. 9-2(a). Here, the P values ​​(upper probability) of the cutting depth ap and feed F are not higher than in FIG. 9-2(a), so they are evaluated with the cutting speed V excluded. And the feed F is excluded because its test result is not significant.

[0068] Figure 9-2(c) shows the results of an analysis of variance to verify whether the cutting depth ap is a significant factor after excluding the feed F in Figure 9-2(b). Here, the P value (upper probability) of the cutting depth ap is not higher than in Figure 9-2(b), so it is evaluated with the feed F excluded. The test result for the cutting depth ap is significant. Therefore, the cutting depth ap is a significant factor.

[0069] Figure 9-3 shows the results of an analysis of variance to exclude one insignificant factor from cutting speed V, cutting depth ap, and feed rate F when machining efficiency is used as an evaluation item. Here, cutting speed V is excluded because its test result is not significant and its P value (upper probability) is the largest.

[0070] Next, an analysis of variance is performed to remove one insignificant factor from the cutting depth ap and feed rate F after removing the cutting speed V in Figure 9-3. However, here, the P value (upper probability) of the cutting depth ap and feed rate F increases compared to Figure 9-3, making the test invalid. Therefore, by returning to the previous state, Figure 9-3, and performing the evaluation, the cutting depth ap and feed rate F become significant factors.

[0071] Figure 9-4 shows the results of an analysis of variance to exclude one insignificant factor from cutting speed V, depth of cut ap, and feed F when surface roughness is used as an evaluation item. Here, the test results for cutting speed V, depth of cut ap, and feed F are not significant, and the P value (upper probability) is 100, so they are not excluded. This is because the P value (upper probability) is calculated using a theoretical formula. In reality, surface roughness does not depend on cutting speed V and depth of cut ap, but is calculated using a formula proportional to the square of feed F. Therefore, only feed F has an effect, and cutting speed V and depth of cut ap can be determined arbitrarily.

[0072] In addition, when product bending is used as an evaluation item, the results of an analysis of variance to exclude one insignificant factor from cutting speed V, cutting depth ap, and feed F are not shown, but tend to be similar to Figures 9-1(a) and (b). That is, cutting speed V is excluded, but cutting depth ap and feed F are not excluded. Therefore, cutting depth ap and feed F become significant factors.

[0073] Furthermore, in an analysis of variance to exclude one insignificant factor from cutting speed V, cutting depth ap, and feed F when tool temperature is used as an evaluation item, the test results for cutting speed V, cutting depth ap, and feed F are 1% significant, 5% significant, and 5% significant, respectively, although not shown. Also, although the P value (upper probability) for cutting depth ap is the largest, since cutting speed V, cutting depth ap, and feed F are all significant, cutting depth ap is not excluded. Therefore, cutting speed V, cutting depth ap, and feed F are all significant factors.

[0074] In the above, the evaluation was performed using the P value of each factor, but the same results can be obtained by using the influence rate or no influence rate of each factor. Here, the influence rate of a certain factor is the ratio of the value obtained by subtracting the P value (upper probability) of that factor from the sum of the P values ​​(upper probability) of all factors to the sum of the P values ​​(upper probability) of all factors. Also, the no influence rate of that factor is the ratio of the P value (upper probability) of that factor to the sum of the P values ​​(upper probability) of all factors.

[0075] Figure 10 is a diagram summarizing the results of Figures 9-1 to 9-4. In the diagram, "◯" indicates that the results were significant, and "×" indicates that the results were not significant.

[0076] Here, since the cutting depth ap and the feed rate F are significant for most of the evaluation items, the presence or absence of an interaction between the cutting depth ap and the feed rate F will be confirmed in the next step 3.

[0077] On the other hand, since the cutting speed V was not significant in the evaluation items of the main force, thrust force, feed force, processing efficiency, surface roughness, and product bending, any value may be set for these evaluation items. However, from the results of step 1, the cutting speed V should be increased in order to increase the processing efficiency, and the effect of the cutting speed V on reducing the main force, thrust force, feed force, surface roughness, and product bending is unclear, and the cutting speed V should be decreased in order to reduce the tool temperature. Considering the processing efficiency, it is advantageous to increase the cutting speed V, but considering the tool temperature, it is advantageous to decrease the cutting speed V, so in the next step 3, a compromise is made and the cutting speed V is set to 120.

[0078] [Step 3 Contents] As described above, step 3 is a step in which the presence or absence of interactions between significant factors is determined using an experimental design method in CAE, and processing conditions in which one level of a significant factor is selected from multiple levels are derived using the significant factors and the presence or absence of interactions between the significant factors. In step 3, the orthogonal array shown in Fig. 7 is also used. Note that it is also possible not to perform the step of determining the presence or absence of interactions between significant factors in step 3.

[0079] FIG. 11 shows the analysis results by CAE in step 3.

[0080] In Fig. 11, the conditions are configured by selecting any of the levels of each factor of the cutting depth ap and the feed F. Specifically, for the cutting depth ap, any of the small level of 0.07, the medium level of 0.175, and the large level of 0.35 is selected. For the feed F, any of the small level of 0.07, the medium level of 0.175, and the large level of 0.42 is selected. Here, the cutting depth ap and the feed F are examples of at least two factors determined as significant factors.

[0081] Incidentally, the conditions newly configured in step 3 are only conditions 13 to 15 enclosed in a thick frame in the figure. Condition 1 and conditions 5 to 9 have already been configured in step 1 or step 2. Therefore, the analysis values ​​of the evaluation items of the main force, thrust force, feed force, machining efficiency, surface roughness, product bending, and tool temperature for condition 1 and conditions 5 to 9 may be those shown in FIG. 3 or FIG. 8. That is, only the analysis values ​​of the evaluation items of the main force, thrust force, feed force, machining efficiency, surface roughness, product bending, and tool temperature for conditions 13 to 15 may be set in FIG. 11. Here, condition 1, conditions 5 to 8 are an example of the first machining condition described in step 1, and condition 9 is an example of the second machining condition described in step 2. Moreover, conditions 13 to 15 are an example of the third machining condition in which one factor is selected from all of the multiple levels of the factor for at least two factors.

[0082] Alternatively, the analytical values ​​of the evaluation items of the main force, thrust force, feed force, machining efficiency, surface roughness, product bending, and tool temperature for condition 1 and condition 5 to condition 9 do not have to be those shown in Fig. 3 or Fig. 8. In this case, condition 1, condition 5 to condition 9, and condition 13 to condition 15 are examples of the third machining conditions in which one factor is selected from all of the multiple levels of the factors for at least two factors.

[0083] 12-1 to 12-4 are diagrams showing a method for determining the presence or absence of significant interactions between factors for each evaluation item based on the analysis results by CAE in step 3. In this embodiment, seven evaluation items are used, but here too, only the main force, feed force, machining efficiency, and surface roughness are shown. The thrust force, product bending, and tool temperature are not shown.

[0084] (a) of each figure (whole figure if not present) shows the results of an analysis of variance for removing one insignificant factor from two factors and the factor of the interaction between these two factors. (b) of each figure (if present) shows the results of an analysis of variance for removing one insignificant factor from the two factors remaining in (a). (c) of each figure (if present) shows the results of an analysis of variance for verifying whether the one factor remaining in (b) is significant.

[0085] In the figures, "**" indicates 1% significance. 1% significance means that the P value, expressed as a percentage, is within 1%. Additionally, "*" indicates 5% significance. 5% significance means that the P value, expressed as a percentage, is within 5%. In what follows, a P value of 5% or less can be considered significant, so both 1% significance and 5% significance will be considered "significant."

[0086] Figure 12-1(a) shows the results of an analysis of variance to exclude one insignificant factor from the cutting depth ap, feed F, and the interaction between the cutting depth and feed ap×F when the principal force is used as the evaluation item. Here, the interaction ap×F is excluded because the test result is not significant and its P value (upper probability) is the largest.

[0087] FIG. 12-1(b) is a diagram showing the results of an analysis of variance to remove one insignificant factor from the cutting depth ap and feed F after removing the interaction ap×F in FIG. 12-1(a). Here, since the P values ​​(upper probability) of the cutting depth ap and feed F are not higher than in FIG. 12-1(a), the evaluation is performed with the interaction ap×F removed. The test results for neither the cutting depth ap nor the feed F are significant. Therefore, none of the cutting depth ap, feed F, or interaction ap×F is significant, and there is no interaction between the cutting depth ap and feed F.

[0088] Furthermore, in an analysis of variance for removing one insignificant factor from the cutting amount ap, feed F, and the interaction ap×F between the cutting amount and feed when thrust force is used as an evaluation item, the interaction ap×F is first removed, although not shown. Next, in an analysis of variance for removing one insignificant factor from the cutting amount ap and feed F after removing the interaction ap×F, the cutting amount ap and feed F become significant and are not removed. Therefore, the interaction ap×F is not significant, and there is no interaction between the cutting amount ap and feed F.

[0089] Figure 12-2(a) shows the results of an analysis of variance to exclude one insignificant factor from the cutting depth ap, feed F, and the interaction between the cutting depth and feed ap×F when feed force is used as the evaluation item. Here, the interaction ap×F is excluded because its test result is not significant and its P value (upper probability) is the largest.

[0090] FIG. 12-2(b) shows the results of an analysis of variance to remove one insignificant factor from the cutting depth ap and feed F after removing the interaction ap×F in FIG. 12-2(a). Here, the P values ​​(upper probability) of the cutting depth ap and feed F are not higher than in FIG. 12-2(a), so they are evaluated with the interaction ap×F removed. And the feed F is removed because its test result is not significant.

[0091] FIG. 12-2(c) shows the results of an analysis of variance to verify whether the cutting depth ap is a significant factor after excluding the feed F in FIG. 12-2(b). Here, since the P value (upper probability) of the cutting depth ap is not higher than in FIG. 12-2(b), it is evaluated with the feed F excluded. The test result for the cutting depth ap is significant. Therefore, the interaction ap×F is not significant, and there is no interaction between the cutting depth ap and the feed F.

[0092] Figure 12-3(a) shows the results of an analysis of variance to exclude one insignificant factor from the cutting depth ap, feed rate F, and the interaction between the cutting depth and feed rate ap×F when machining efficiency is used as an evaluation item. Here, the interaction ap×F is excluded because its test result is not significant and its P value (upper probability) is the largest.

[0093] FIG. 12-3(b) shows the results of an analysis of variance to exclude one insignificant factor from the cutting depth ap and feed rate F after excluding the interaction ap×F in FIG. 12-3(a). Here, the P values ​​(upper probability) of the cutting depth ap and feed rate F are not higher than in FIG. 12-3(a), so they are evaluated with the interaction ap×F excluded. Then, the cutting depth ap is excluded because its test result is not significant and its P value (upper probability) is the largest.

[0094] Next, an analysis of variance is performed to verify whether the feed rate F is a significant factor after excluding the cutting depth ap in Fig. 12-3(b). However, here, the P value (upper probability) of the feed rate F is higher than in Fig. 12-3(b), making the test invalid. Therefore, by returning to the previous state, Fig. 12-3(b), and performing the evaluation, it is found that the cutting depth ap, the feed rate F, and the interaction ap×F are all insignificant, and there is no interaction between the cutting depth ap and the feed rate F.

[0095] Figure 12-4 shows the results of an analysis of variance to exclude one insignificant factor from the cutting depth ap, feed F, and the interaction between the cutting depth and feed ap×F when surface roughness is used as an evaluation item. Here, the test results for the cutting depth ap, feed F, and the interaction between the cutting depth and feed ap×F are not significant, and the P value (upper probability) is 100, so they are not excluded. This is because the P value (upper probability) is calculated using a theoretical formula. In reality, surface roughness does not depend on the cutting depth ap, but is calculated using a formula proportional to the square of the feed F. Therefore, only the feed F has an effect, and the cutting depth ap can be determined arbitrarily.

[0096] Furthermore, when product bending is used as an evaluation item, the results of an analysis of variance to exclude one insignificant factor from the cutting amount ap, feed F, and the interaction ap×F between the cutting amount and feed are not shown, but tend to be similar to those in Figures 12-1(a) and (b). That is, the interaction ap×F is excluded, but the cutting amount ap and feed F are not excluded. Therefore, none of the cutting amount ap, feed F, or interaction ap×F is significant, and there is no interaction between the cutting amount ap and feed F.

[0097] Furthermore, in an analysis of variance for removing one insignificant factor from the cutting amount ap, feed F, and the interaction ap×F between the cutting amount and feed when tool temperature is used as an evaluation item, the interaction ap×F is first removed, although not shown. Next, in an analysis of variance for removing one insignificant factor from the cutting amount ap and feed F after removing the interaction ap×F, the cutting amount ap and feed F become significant and are not removed. Therefore, the interaction ap×F is not significant, and there is no interaction between the cutting amount ap and feed F.

[0098] In the above, the evaluation was performed using the P value of each factor, but the same results can be obtained by using the influence rate or no influence rate of each factor. Here, the influence rate of a certain factor is the ratio of the value obtained by subtracting the P value (upper probability) of that factor from the sum of the P values ​​(upper probability) of all factors to the sum of the P values ​​(upper probability) of all factors. Also, the no influence rate of that factor is the ratio of the P value (upper probability) of that factor to the sum of the P values ​​(upper probability) of all factors.

[0099] Figure 13 is a diagram summarizing the results of Figures 12-1 to 12-4. In the diagram, "◯" indicates that the results were significant, and "×" indicates that the results were not significant.

[0100] Here, since the interaction ap×F is not significant for all evaluation items, there is no interaction for any evaluation items for the cutting depth ap and feed F. Therefore, the conditions for the cutting depth ap and feed F can be determined individually. Specifically, the optimal machining conditions for each factor are determined based on the results of step 1.

[0101] Fig. 14 shows the method of determining the optimal processing conditions for each factor for each evaluation item. For factors for which conditions may be freely set, all levels of the factor are hatched with diagonal lines. For factors for which conditions should be fixed, the levels at which the factors should be fixed are cross-hatched.

[0102] FIG. 14(a) shows a method for determining the optimal machining conditions of cutting speed V, cutting depth ap, and feed F when the principal component force is used as the evaluation item. In FIG. 13, when the principal component force is used as the evaluation item, cutting speed V is marked as "x", so cutting speed V is a factor for which the conditions may be freely set. In addition, in FIG. 13, when the principal component force is used as the evaluation item, cutting depth ap is marked as "○", so cutting depth ap is a factor for which it is recommended to fix the conditions. In this case, from the relationship between cutting depth ap and principal component force obtained in step 1 (see FIG. 5-1(a)), it can be seen that the cutting depth ap should be reduced to reduce the principal component force, so cutting depth ap is set to the smaller value here. In addition, when the principal component force is used as the evaluation item in FIG. 13, feed F is marked as "○", so feeding F is a factor for which it is recommended to fix the conditions. In this case, from the relationship between the feed F and the principal force calculated in step 1 (see Figure 6-1(a)), it can be seen that in order to reduce the principal force, the feed F should be reduced, so here the feed F is set to the smaller value.

[0103] FIG. 14(b) shows a method for determining the optimal machining conditions of the cutting speed V, the cutting depth ap, and the feed F when the thrust force is used as the evaluation item. In FIG. 13, when the thrust force is used as the evaluation item, the cutting speed V is marked as "x", so the cutting speed V is a factor whose conditions may be freely set. In addition, in FIG. 13, when the principal force is used as the evaluation item, the cutting depth ap is marked as "○", so the cutting depth ap is a factor whose conditions are recommended to be fixed. In this case, it is understood that the cutting depth ap can be reduced to reduce the thrust force from the relationship between the cutting depth ap and the thrust force obtained in step 1, so the cutting depth ap is set to the smaller value here. Furthermore, in FIG. 13, when the thrust force is used as the evaluation item, the feed F is marked as "○", so the feed F is a factor whose conditions are recommended to be fixed. In this case, it is understood that the feed F can be reduced to reduce the thrust force from the relationship between the feed F and the thrust force obtained in step 1, so the feed F is set to the smaller value here.

[0104] FIG. 14(c) shows a method for determining the optimal machining conditions of the cutting speed V, the cutting depth ap, and the feed F when the feed force is used as the evaluation item. In FIG. 13, when the feed force is used as the evaluation item, the cutting speed V is marked as "x", so the cutting speed V is a factor whose conditions may be set freely. In addition, in FIG. 13, when the feed force is used as the evaluation item, the cutting depth ap is marked as "o", so the cutting depth ap is a factor whose conditions are recommended to be fixed. In this case, from the relationship between the cutting depth ap and the feed force obtained in step 1 (see FIG. 5-2(a)), it can be seen that the cutting depth ap should be reduced to reduce the feed force, so here the cutting depth ap is set to the smaller value. In addition, in FIG. 13, when the feed force is used as the evaluation item, the feed F is marked as "x", so the feed F is a factor whose conditions may be set freely.

[0105] FIG. 14(d) shows a method for determining the optimal machining conditions of the cutting speed V, the cutting depth ap, and the feed F when the machining efficiency is used as the evaluation item. In FIG. 13, when the machining efficiency is used as the evaluation item, the cutting speed V is marked as "x", so the cutting speed V is a factor whose conditions may be freely set. In addition, in FIG. 13, when the machining efficiency is used as the evaluation item, the cutting depth ap is marked as "○", so the cutting depth ap is a factor whose conditions are recommended to be fixed. In this case, the relationship between the cutting depth ap and the machining efficiency obtained in step 1 (see FIG. 5-3(a)) shows that the cutting depth ap should be increased to increase the machining efficiency, so the cutting depth ap is set to the larger value here. Furthermore, in FIG. 13, when the machining efficiency is used as the evaluation item, the feed F is marked as "○", so the feed F is a factor whose conditions are recommended to be fixed. In this case, the relationship between the feed F and the machining efficiency obtained in step 1 (see FIG. 6-3(a)) shows that the feed F should be increased to increase the machining efficiency, so the feed F is set to the larger value here.

[0106] FIG. 14(e) shows a method for determining the optimal machining conditions of the cutting speed V, the cutting depth ap, and the feed F when surface roughness is used as the evaluation item. In FIG. 13, when surface roughness is used as the evaluation item, the cutting speed V is marked as "x", so the cutting speed V is a factor whose conditions may be set freely. In FIG. 13, when surface roughness is used as the evaluation item, the cutting depth ap is marked as "x", so the cutting depth ap is a factor whose conditions may be set freely. In FIG. 13, when surface roughness is used as the evaluation item, the feed F is marked as "o", so the feed F is a factor whose conditions are recommended to be fixed. In this case, from the relationship between the feed F and the surface roughness obtained in step 1 (see FIG. 6-4(a)), it can be seen that the feed F should be reduced to reduce the surface roughness, so the feed F is set to the smaller value here.

[0107] FIG. 14(f) shows a method for determining the optimal machining conditions of the cutting speed V, the cutting depth ap, and the feed F when the product bending is used as the evaluation item. In FIG. 13, when the product bending is used as the evaluation item, the cutting speed V is marked as "x", so the cutting speed V is a factor for which the conditions may be freely set. In addition, in FIG. 13, when the product bending is used as the evaluation item, the cutting depth ap is marked as "○", so the cutting depth ap is a factor for which it is recommended to fix the conditions. In this case, it is understood that the cutting depth ap can be reduced to reduce the product bending from the relationship between the cutting depth ap and the product bending obtained in step 1, so the cutting depth ap is set to the smaller value here. Furthermore, in FIG. 13, when the product bending is used as the evaluation item, the feed F is marked as "○", so the feed F is a factor for which it is recommended to fix the conditions. In this case, it is understood that the cutting depth F can be reduced to reduce the product bending from the relationship between the feed F and the product bending obtained in step 1, so the feed F is set to the smaller value here.

[0108] FIG. 14(g) shows a method for determining the optimal machining conditions of the cutting speed V, the cutting amount ap, and the feed F when the tool temperature is used as the evaluation item. In FIG. 13, when the tool temperature is used as the evaluation item, the cutting speed V is marked as "○", so the cutting speed V is a factor whose condition is recommended to be fixed. In this case, it is understood that the cutting speed V should be reduced to lower the tool temperature from the relationship between the cutting speed V and the tool temperature obtained in step 1, so the cutting speed V is set to the smaller value here. In addition, when the tool temperature is used as the evaluation item in FIG. 13, the cutting amount ap is marked as "○", so the cutting amount ap is a factor whose condition is recommended to be fixed. In this case, it is understood that the cutting amount ap should be reduced to lower the tool temperature from the relationship between the cutting amount ap and the tool temperature obtained in step 1, so the cutting amount ap is set to the smaller value here. In addition, when the tool temperature is used as the evaluation item in FIG. 13, the feed F is marked as "○", so the feed F is a factor whose condition is recommended to be fixed. In this case, from the relationship between the feed F and the tool temperature obtained in step 1, it can be seen that the tool temperature can be lowered by reducing the feed F, so here the feed F is set to the smaller value.

[0109] In this way, the optimal processing conditions for each factor are determined for each evaluation item, but at this point, the optimal processing conditions may differ for each evaluation item. In fact, even in Figures 14(a) to (g), the optimal processing conditions are the same when the main force, thrust force, and product bending are used as evaluation items, but the optimal processing conditions for the other items are different.

[0110] Therefore, in step 3, using the optimal processing conditions for each factor determined for each evaluation item, the optimal processing conditions appropriate for all evaluation items are determined as the final optimal processing conditions, either by human judgment or software processing.

[0111] [Step 4 Contents] As described above, step 4 is a step in which actual machining is performed using the machining conditions to confirm the appropriateness of the machining conditions. Specifically, in step 4, actual machining is performed with an actual machine using the optimal machining conditions derived in step 3, and it is verified whether the relationship between the actual measurement results from the actual machining obtained in step 1 and the analysis results from CAE is maintained.

[0112] [Additional Steps] After steps 1 to 4 are performed, an additional step may be performed in which a workpiece is manufactured using the processing conditions whose appropriateness has been confirmed in step 4.

[0113] [Example of the configuration of the machining condition derivation device] The above-mentioned steps 1 to 3 can be automatically performed by software. Here, the machining condition deriving device will be described assuming that the machining condition deriving device performs the processes of steps 1 to 3.

[0114] 15 is a block diagram showing an example of a functional configuration of the machining condition deriving device 10 in this embodiment. As shown in the figure, the machining condition deriving device 10 includes an actual measurement result storage unit 11, a verification unit 12, an analysis result storage unit 13, an orthogonal table storage unit 14, a first determination unit 15, a first determination result storage unit 16, a second determination unit 17, a second determination result storage unit 18, and a derivation unit 19.

[0115] The actual measurement result storage unit 11 stores the actual measurement results obtained by the actual machining shown in Fig. 2. That is, the actual measurement result storage unit 11 may store in advance the actual measurement results obtained by performing actual machining with an actual machine under preconfigured conditions.

[0116] The verification unit 12 executes the above-mentioned step 1. That is, the verification unit 12 performs an analysis by CAE for the preconfigured conditions, and creates the analysis result by CAE shown in Fig. 3. Then, the verification unit 12 verifies the validity of the analysis result by CAE for the actual measurement results stored in the actual measurement result storage unit 11. The verification unit 12 holds, as the verification results, for example, the graphs shown in Figs. 4-1 to 4-4, Figs. 5-1 to 5-4, and Figs. 6-1 to 6-4.

[0117] The analysis result storage unit 13 stores the analysis results generated by the verification unit 12 using CAE.

[0118] The orthogonal table storage unit 14 stores the orthogonal table shown in FIG.

[0119] The first determination unit 15 executes the above-mentioned step 2. That is, the conditions configured from the orthogonal array stored in the orthogonal array storage unit 14 are analyzed by CAE, and the analysis result by CAE shown in Fig. 8 is created. At that time, if the conditions configured from the orthogonal array include a condition already configured in step 1, the analysis result stored in the analysis result storage unit 13 is used as the analysis result for that condition. Then, the first determination unit 15 performs an analysis of variance to determine significant factors using the analysis result by CAE shown in Fig. 8, and creates the analysis of variance results shown in Figs. 9-1 to 9-4 and 10.

[0120] The first determination result storage unit 16 stores the analysis results by CAE and the results of the ANOVA generated by the first determination unit 15.

[0121] The second determination unit 17 executes the first stage of step 3 described above. That is, the conditions configured from the orthogonal array stored in the orthogonal array storage unit 14 are analyzed by CAE, and the analysis result by CAE shown in FIG. 11 is created. At that time, if the conditions configured from the orthogonal array include a condition already configured in step 1 or step 2, the analysis result stored in the analysis result storage unit 13 or the analysis result stored in the first determination result storage unit 16 is used as the analysis result for that condition. Then, the second determination unit 17 performs an analysis of variance to determine the presence or absence of a significant interaction between factors, using the analysis result by CAE shown in FIG. 11, and creates the analysis of variance results shown in FIG. 12-1 to FIG. 12-4 and FIG. 13.

[0122] The second determination result storage unit 18 stores the analysis results by CAE and the analysis of variance created by the second determination unit 17.

[0123] The derivation unit 19 executes the latter part of step 3 described above. That is, the derivation unit 19 derives the optimal processing conditions for each evaluation item shown in Fig. 14(a) to (g) by referring to the variance analysis results stored in the second determination result storage unit 18, the graph of the verification results stored in the verification unit 12, and the like, and stores the derived optimal processing conditions. Then, the derivation unit 19 performs some software processing using the optimal processing conditions for each evaluation item shown in Fig. 14(a) to (g), thereby determining the optimal processing conditions appropriate for all evaluation items as the final optimal processing conditions.

[0124] Among these functions, the verification unit 12, the first determination unit 15, the second determination unit 17, and the derivation unit 19 are realized by a CPU (not shown) of the machining condition deriving device 10 reading a program stored in a HDD (not shown) or the like into a RAM (not shown). Also, the actual measurement result storage unit 11, the analysis result storage unit 13, the orthogonal array storage unit 14, the first determination result storage unit 16, and the second determination result storage unit 18 are realized by the HDD (not shown) and RAM (not shown) of the machining condition deriving device 10.

[0125] [Advantages of this embodiment] In this embodiment, first, the validity of CAE for the actual machining of the workpiece is verified. Next, in a state where the validity of CAE is verified, a significant factor among a plurality of factors in the machining of the workpiece is determined in CAE using an experimental design method. Next, in CAE, the presence or absence of an interaction between significant factors is determined using an experimental design method, and machining conditions in which one level is selected from a plurality of levels of a significant factor are derived using the significant factors and the presence or absence of an interaction between the significant factors. Finally, actual machining is performed using the machining conditions, and the appropriateness of the machining conditions is confirmed. As a result, in this embodiment, it has become possible to appropriately determine the machining conditions to be used in the actual machining of the workpiece in a short time. [Explanation of symbols]

[0126] 10... Machining condition derivation device, 11... Actual measurement result storage unit, 12... Verification unit, 13... Analysis result storage unit, 14... Orthogonal array storage unit, 15... First determination unit, 16... First determination result storage unit, 17... Second determination unit, 18... Second determination result storage unit, 19... Derivation unit

Claims

1. a step of verifying the validity of the CAE for the actual machining of the workpiece by evaluating a relationship between an actual measurement result of the actual machining and an analysis result by the CAE, and linking the actual machining with the CAE; determining a significant factor among a plurality of factors in processing the workpiece by using an experimental design method in the CAE in a state where the validity of the CAE is verified; In the CAE, using an experimental design method, determining the presence or absence of interactions between the significant factors, and deriving processing conditions in which one level of the significant factor is selected from multiple levels using the significant factors and the presence or absence of interactions between the significant factors; performing the actual machining using the machining conditions to confirm the appropriateness of the machining conditions; manufacturing the workpiece using the processing conditions whose suitability has been confirmed; A method for manufacturing a workpiece, comprising:

2. 2. The method for manufacturing a workpiece according to claim 1, wherein the verifying step includes identifying a first machining condition among all machining conditions in which one level is selected from a plurality of levels of each of the plurality of factors for each of the plurality of factors, and verifying the validity of a result of a simulation by the CAE using the first machining condition against a result of the actual machining using the first machining condition.

3. 3. The method for manufacturing a workpiece according to claim 2, wherein in the determining step, the significant factors are determined based on a result of a simulation by the CAE using second processing conditions in which one level is selected from a portion of multiple levels of each of the plurality of factors for each of the plurality of factors.

4. The method for manufacturing a workpiece according to claim 3 , wherein in the determining step, the significant factor is determined further based on a result of a simulation by the CAE using the first processing condition in the verifying step.

5. A method for manufacturing a workpiece as described in claim 3, wherein in the deriving step, the presence or absence of the interaction is determined based on the results of a simulation by the CAE using third processing conditions in which one factor is selected from all of the multiple levels of each of the significant factors.

6. A method for manufacturing a workpiece as described in claim 5, wherein in the derivation step, the presence or absence of the interaction is determined further based on the results of the simulation by the CAE using the first processing conditions in the verification step and the results of the simulation by the CAE using the second processing conditions when determining the significant factors.

7. 3. The method for manufacturing a workpiece according to claim 2, wherein in the deriving step, one level is selected from a plurality of levels of the significant factor based on a relationship between a result of the actual machining using the first machining conditions in the verifying step and a result of the simulation by the CAE.

8. a step of verifying the validity of the CAE for the actual machining of the workpiece by evaluating a relationship between an actual measurement result of the actual machining and an analysis result by the CAE, and linking the actual machining with the CAE; determining a significant factor among a plurality of factors in processing the workpiece by using an experimental design method in the CAE in a state where the validity of the CAE is verified; In the CAE, using an experimental design method, determining the presence or absence of interactions between the significant factors, and deriving processing conditions in which one level of the significant factor is selected from multiple levels using the significant factors and the presence or absence of interactions between the significant factors; and performing the actual machining using the machining conditions to confirm the appropriateness of the machining conditions.

9. a verification means for verifying the validity of the CAE for the actual machining of the workpiece by evaluating a relationship between an actual measurement result of the actual machining and an analysis result by the CAE, and linking the actual machining with the CAE; a determining means for determining a significant factor among a plurality of factors in the processing of the workpiece by using an experimental design method in the CAE in a state where the validity of the CAE is verified; a derivation means for determining the presence or absence of an interaction between the significant factors using an experimental design method in the CAE, and deriving processing conditions in which one level of the significant factor is selected from a plurality of levels using the significant factor and the presence or absence of an interaction between the significant factors; A processing condition deriving device comprising:

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