JP7178508B2 - PEAN FORMING CONDITION SETTING METHOD, PEAN FORMING METHOD, AND PEAN FORMING CONDITION SETTING DEVICE - Google Patents

Description

The present invention relates to a pean forming condition setting method, a pean forming method, and a pean forming condition setting device.

Conventionally, the setting of plate peen forming conditions has been carried out using, as indices, the amounts of deformation of a plurality of regions partitioned on the surface of the member to be formed before and after forming.

In Patent Document 1, in a peen forming method, a metal member is divided into a plurality of regions, and a processing area dividing step of obtaining the plate thickness and curvature to be formed for each region, and a plurality of test pieces having different plate thicknesses. By referring to projection condition data summarizing shot projection conditions for giving a predetermined curvature, projection conditions corresponding to combinations of plate thicknesses and curvatures to be formed for each region are obtained, and based on the obtained projection conditions. An invention is disclosed having a molding step that molds each region with a .

In Patent Document 2, the surface of a member is divided into a plurality of regions, temporary molding conditions are set for each region, and the deformation amount in one region when molded under the temporary molding conditions affects the deformation amount in the other regions. An invention is disclosed in which the degree of influence is calculated for each region as relational information, and the peen forming conditions for one region and the other region for achieving the deformation amount of the target shape are set based on the relational information.

Japanese Patent Application Laid-Open No. 2003-220428 JP 2019-155387 A

However, in the peen forming method described in Patent Document 1, the projection conditions optimized for the combination of plate thickness and curvature for each region are used as they are as the projection conditions for each region in the entire molding. Further, in the peen forming method described in Patent Document 2, only the amount of deformation for each region for forming the member into the shape after forming is used as an index. As a result, there is a case where the accuracy of molding is lowered at a portion of the molding shape that particularly requires accuracy.

A peen forming condition setting method according to the present disclosure for achieving the above object divides an analysis model of a member to be formed into a plurality of individual regions, and performs analysis based on predetermined temporary forming conditions, thereby forming a temporary forming shape. obtaining the amount of deviation of the individual region from the reference shape and the amount of deformation of the individual region for the provisional forming shape; and the provisional forming condition, the amount of separation, and the amount of deformation obtaining a relational expression, wherein, of the deviation amount and the deformation amount calculated by substituting the temporary molding condition into the relational expression, the deviation amount is the smallest and the deformation amount with respect to the target deformation amount. and setting the temporary molding conditions that have the smallest difference in the optimum molding conditions.

A molding method according to the present disclosure for achieving the above object has a step of peen-molding the member to be molded under the optimum molding conditions set by the peen molding condition setting method.

A peen forming condition setting device for setting peen forming conditions, in which a provisional forming shape obtained by dividing an analysis model of a member to be formed into a plurality of regions and executing an analysis based on predetermined provisional forming conditions means for acquiring the amount of deviation of the individual region from the reference shape and the amount of deformation of the individual region, and acquiring a relational expression between the temporary molding condition, the amount of deviation, and the amount of deformation; means for calculating the amount of deviation and the amount of deformation by substituting the temporary molding conditions of, and the amount of deviation is the smallest among the amount of deviation and the amount of deformation calculated, and and means for setting the temporary molding conditions under which the difference in the amount of deformation is the smallest as optimum molding conditions.

According to the present invention, since the forming conditions under which the amount of deviation of the formed shape by peen forming from the reference shape and the difference between the target deformation amount and the deformation amount are minimized are set as the optimum forming conditions, forming is performed under the optimum forming conditions. It is possible to improve the accuracy of the target molding shape as a whole of the formed molding shape.

FIG. 1 is a diagram showing an analysis model divided into a plurality of regions of a member to be molded according to an embodiment of the present invention. FIG. 2 is a block diagram showing a peen forming system including a forming condition setting device according to an embodiment of the present invention. FIG. 3 is a diagram showing deviation amounts in the molded shape of the molded member according to the embodiment of the present invention. FIG. 4 is a diagram showing the arc height, which is the amount of deformation in the molded shape of the molded member according to the embodiment of the present invention. FIG. 5 is a flow chart showing a method for setting peen forming conditions according to an embodiment of the present invention. FIG. 6 illustrates a molded member with a reinforcing member according to an embodiment of the present invention. FIG. 7 is a diagram showing details of an analysis model in which a molded member having reinforcing members according to an embodiment of the present invention is divided into a plurality of regions.

<About pean molding>
Pean forming is a process in which a plurality of small grained steel balls are projected onto the surface of a plate-shaped member, etc., and the pressure from the projection causes plastic deformation from the surface side of the plate-shaped member, resulting in bending deformation of the plate-shaped member. is. A peen-processed plate-shaped member has a so-called isotropic strain that, in principle, produces a curved shape in which the processed surface side protrudes from the processing center. Forming conditions used in the peen forming process include, for example, the speed of projecting steel balls, the amount of shot per hour, and the moving speed of the shot projecting part for projecting steel balls. By changing these forming conditions, Molding shape can be adjusted.

Embodiments of the present invention will be described in detail below with reference to the drawings. It should be noted that the present invention is not limited only to the examples described below.

A member P to be formed according to the present invention is, as shown in FIG. 1, a long plate-like member, and the material thereof is a metal containing, for example, an aluminum alloy. The member to be molded P according to the present embodiment is used for a part constituting a wing body of an airplane.

<Pean molding system>
The peen forming system 100 has, as shown in FIG. 2, a pean forming condition setting device 200 that sets pean forming conditions R, and a pean forming device 300 that actually performs pean forming processing. In the pean forming system 100, the peen forming condition setting device 200 sets peen forming conditions R for peen forming the member P to be formed, and the pean forming device 300 forms the peen forming conditions R based on the pean forming conditions. A peen forming process is performed on the member P to obtain a formed shape. In this embodiment, the peen forming condition R is composed of the projecting speed of projecting steel balls, the amount of projecting per hour, and the moving speed of the shot projecting unit 350 with respect to the member to be formed P, which will be described later. Note that the peen forming condition R is not limited to these configurations. A pean forming condition setting device 200 and a pean forming device 300 that constitute the pean forming system 100 will be described below with reference to FIG. 2 . Note that the pean forming condition setting device 200 and the pean forming device 300 may be configured integrally.

<Pean molding condition setting device>
As shown in FIG. 2, the pean forming condition setting device 200 includes an arithmetic unit 210 for arithmetic processing, an input/output unit 220 for input/output processing with the outside, and a recording unit 230 for recording programs and data. I have. Recording unit 230 and input/output unit 220 are each connected to computing unit 210 . The peen forming condition setting device 200 is configured as a computer terminal, and the input/output unit 220 may be provided with, for example, a keyboard, a mouse, and a display monitor as external input devices.

The arithmetic unit 210 performs arithmetic processing based on the input from the input/output unit 220 and the program and data of the recording unit 230 . The calculation unit 210 outputs the set peen forming condition R to the input/output unit 220 and controls the entire pean forming condition setting device 200 . The calculation unit 210 is configured as a CPU.

The recording unit 230 records programs and data. As shown in FIG. 2, the recording unit 230 includes a region division program 232, a provisional molding condition setting program 234, a shape acquisition program 236, a divergence amount acquisition program 238, a deformation amount acquisition program 240, a learning model program 242, and a relational expression acquisition program 244. , a calculation program 246 and a molding condition setting program 248 . The recording unit 230 also has a molding result database 250 . Recording unit 230 is configured by a memory and an HDD.

The area partitioning program 232 partitions a plurality of areas on the front surface and the back surface of the analysis model M based on the input analysis model M of the member to be molded P and the target molding shape H. The analysis model M is the shape of the member to be molded P before molding, and is given by CAD data, for example. The analysis model M has information on materials. The target forming shape H is a model of the shape to be obtained by peen forming, and is given by CAD data, for example. The division of a plurality of regions into the analysis model M is performed, for example, according to the material, plate thickness, and curvature, and the division of a plurality of regions into the analysis model M is often performed, for example, for locations with large changes in shape and curvature. may be partitioned, and fewer regions may be partitioned for locations where the change in shape or curvature is small.

The provisional molding condition setting program 234 sets a plurality of provisional molding conditions T. FIG. In this embodiment, the temporary molding condition T is composed of the projecting speed of the steel balls, the amount of projecting per hour, and the moving speed of the shot projecting section 330 . Note that the temporary molding condition T is not limited to these configurations.

The shape acquisition program 236 simulates peen forming processing based on the input analysis model and forming conditions, and outputs a forming shape (temporary forming shape). That is, the shape acquisition program 236 outputs the entire temporary forming shape B, which is the forming shape for the analysis model M, when the analysis model M and the provisional forming conditions T are input. The shape acquisition program 236 uses, for example, the finite element method as a method for simulating the peen forming process.

The divergence amount acquisition program 238 acquires the divergence amount G of the individual region A in the forming shape with respect to the set region U of the reference shape S. Here, as shown in FIG. 3, the reference shape S has a surface shape after molding, and the reference shape S has a set region U defined therein. Here, the setting area U of the reference shape S is configured to include a plurality of individual areas. 3 and 4, the divergence amount G means the distance of the center point AC of the individual area A of the forming shape from the setting area U of the reference shape S in the plane direction. The reference shape S, as shown in FIG. 3, may be a mold shape for the molding shape. The reference shape S is created by CAD data, for example.

The deformation amount acquisition program 240 acquires the deformation amount D of each individual region before and after the analysis model M is molded. The deformation amount D is the amount of deformation of the center point AC of the individual region A due to the peen forming process. In this embodiment, the deformation amount D indicates the arc height in each individual region. In this embodiment, since the shape of the member to be molded P before molding is flat, the arc height can be obtained from the shape after molding. As shown in FIG. 4, the arc height is the center point AC of the surface of the individual region A in the shape after molding, and the midpoints of two sides DX1 and DX2 parallel to the X-axis direction of the sides forming the individual region A. A distance HX from a line AX connecting XM1 and XM2, and a distance HY from a line AY connecting midpoints YM1 and YM2 of two sides DY1 and DY2 parallel to the Y-axis direction. The amount of deformation D is not necessarily limited to the arc height as long as it represents the deformation of the individual region A due to the peen forming process.

The learning model program 242 obtains a learning model L for deriving the amount of deviation G and the amount of deformation D from the molding condition from the data group of the amount of deviation G and the amount of deformation D of the molding condition. Note that, in this embodiment, the learning model L is included in the relational expression F described later. The learning model L is a weighted function for calculating the amount of divergence G and the amount of deformation D, which are objective variables, from the molding conditions, which are explanatory variables. The learning model L is, for example, a weighted function for calculating the amount of deviation G and the amount of deformation D from the temporary molding conditions T in the overall temporary molding shape B. FIG. The learning model L is acquired as, for example, a neural network.

The learning model L may be acquired as a learning model L after performing machine learning as so-called supervised learning on the correct data K including the molding condition, the deviation amount G, the deformation amount D, and the molding quality result J. Machine learning based on supervised learning enables the learning model L to calculate highly accurate data by correcting the weights so that the amount of deviation G and the amount of deformation D calculated from given molding conditions are correct. can. Here, the forming result data includes the peen forming conditions R for the actual peen forming process, the deviation amount G from the reference shape S, the deformation amount D, and the forming quality result J, and can be used as so-called correct answer data K. can. The learning model program 242 can use molding result data recorded in a molding result database 250, which will be described later.

The relational expression acquisition program 244 acquires the relational expression F from the data group of the molding conditions, the amount of divergence G, and the amount of deformation D. The relational expression F is obtained by determining the correlation using the amount of deviation G and the amount of deformation D as objective variables and the molding shape as explanatory variables. The relational expression F is obtained by, for example, multiple regression analysis of the data group of the molding conditions, the amount of divergence G, and the amount of deformation D. The relational expression acquisition program 244 can use molding result data recorded in a molding result database 250, which will be described later.

As an example of the case where the relational expression F is obtained by multiple regression analysis, an expression expressing the arc heights HX and HY as the amount of deviation G and the amount of deformation D according to the provisional molding conditions T is shown below.
G= _C0 + _C1.T1 + _C2.T2 (1)
_HX = _C3 + _C4.T1 + _C5.T2 (2)
HY= _C6 + _C7.T1 + _C8.T2 (3)
Here, T1 is a temporary molding condition for shot projection onto the front surface of the individual region A, and T2 is a temporary molding condition for shot projection onto the back surface of the individual region A. C ₀ to C ₈ are weights obtained by multiple regression analysis or the like.

The calculation program 246 substitutes a predetermined range of molding conditions into the relational expression F to calculate the amount of divergence G and the amount of deformation D. Here, as described above, in this embodiment, since the relational expression F includes the learning model L, the deviation amount G and the deformation amount D are calculated by substituting the molding conditions into the relational expression F in the following description. In some cases, the case where the deviation amount G and the deformation amount D are acquired by substituting the molding conditions into the learning model L is also included.

The molding condition setting program 248 determines whether the divergence amount G calculated by the calculation program 246 and the difference between the target deformation amount and the deformation amount D are minimum. When a plurality of divergence amounts G and deformation amounts D exist, the forming condition in which the difference between the divergence amount G and the target deformation amount and the deformation amount D is the smallest is defined as the optimum peen forming condition R. set.

In addition, the molding condition setting program 248 may be given a threshold for judging that it is the minimum. In this case, the molding condition setting program 248 can determine that the deviation amount G and the difference between the target deformation amount and the deformation amount D are equal to or less than the threshold value, and the molding condition in this case is set to the optimum peening condition. A molding condition R is set.

The molding result database 250 has information on the molded member actually peen-molded, material, molding shape, molding conditions, and data on the quality of the processing result. The forming result database 250 may be configured such that the forming result of the peen forming device 300 is input to the peen forming condition setting device 200 via the input/output unit 220 and recorded in the forming result database 250 .

The input/output unit 220 performs input/output processing of data with the outside of the peen forming condition setting device 200 based on instructions from the calculation unit 210 . The input/output unit 220 is connected to the input/output unit 320 of the peen forming device 300, as shown in FIG. The input/output unit 220 is wired to a LAN (not shown). The input/output unit 220 may be wirelessly connected by WiFi or the like to input/output data, or may input/output data via a portable recording medium such as a USB memory.

<Pean forming device>
The peen forming device 300 actually performs peen forming based on peen forming conditions R set for the member P to be formed. The peen forming apparatus 300 includes a control section 310, an input/output section 320, a shot projection section 330 and a recording section 340, as shown in FIG. The peen forming process in this embodiment is performed by moving the shot projection part 330 at a predetermined movement speed with respect to the member P to be formed attached to the peen forming device 300 . The peen forming process may be performed by moving the member P to be formed with respect to the shot projection section 330 at a predetermined moving speed.

The control unit 310 controls the operation of the shot projection unit 330 based on the set peen forming condition R, and also performs overall control of the peen forming apparatus 300 . The control unit 310 includes a CPU, memory, and HDD (not shown). Furthermore, the control unit 310 may include a display monitor that displays the status of the peen forming process.

The input/output unit 320 receives the pean forming conditions R from the pean forming condition setting device 200, and also performs data input/output processing with the outside. The input/output unit 320 is wired to a LAN (not shown). The input/output unit 320 may be wirelessly connected by WiFi or the like to input/output data, or may input/output data via a portable recording medium such as a USB memory. .

The shot projection unit 330 actually performs peen forming processing on the plate-like member based on the peen forming conditions R instructed by the control unit 310 . The shot projection unit 330 is configured to shoot steel balls based on the peen forming condition R, and the projection speed of the steel balls, the amount of shot per hour, and the movement speed of the shot projection unit 330 can be adjusted. ing. In addition, when the member P to be molded moves with respect to the shot projection part 330, the moving speed is the moving speed of the member P to be molded.

<Molding method>
A method for molding the member to be molded P will be described based on the flowchart shown in FIG. Here, the molding method is roughly divided into three stages. That is, the first step is the step of deriving the relational expression F for calculating the molding conditions (S10 to S50), and the second step is the step of calculating the peen forming condition R from the relational expression F. (S60 to S80), and the third stage is a stage (S90) in which peen forming processing is actually performed on the plate-like member based on the calculated peen forming conditions R. Each step will be described in detail below.

<Acquisition of relational expression>
The step of obtaining the relational expression F will be described. As shown in FIG. 5, the step of obtaining the relational expression F includes a step of dividing a plurality of regions into the analysis model M of the member to be molded P (S10) and a step of setting the temporary molding conditions T (S20). , the step of acquiring the overall temporary molding shape B of the member to be molded P (S30), the step of acquiring the deviation amount G and the deformation amount D of the overall temporary molding shape B from the reference shape S (S40), the molding conditions, the deviation and obtaining a relational expression F of the amount G and the amount of deformation D (S50).

In the step (S10) of dividing a plurality of areas into the analysis model M of the member P to be molded P, based on the input member P and the target molding shape H, the front and back surfaces of the analysis model M of the member P are Divide into multiple areas. Partitioning of the analysis model M into a plurality of regions is performed by the region partitioning program 232 .

In the step of setting the temporary molding conditions T (S20), the temporary molding conditions T are set for the analysis model M in which the regions are divided. Acquisition of the temporary molding conditions T is performed by the temporary molding condition setting program 234 .

In the step (S30) of acquiring the overall temporary molding shape B of the member P to be molded P, a plurality of overall temporary molding shapes B are acquired by performing a numerical analysis simulation for the entire analysis model M based on a plurality of temporary molding conditions T. . Acquisition of the overall temporary molding shape B is performed by the shape acquisition program 236 .

In the step (S40) of acquiring the amount of deviation G and the amount of deformation D of the overall provisional forming shape B from the reference shape S, the provisional forming condition T, the amount of deviation G from the reference shape S, and A deformation amount D is acquired. Acquisition of the deviation amount G is performed by the deviation amount acquisition program 238 , and acquisition of the deformation amount D is performed by the deformation amount acquisition program 240 .

In the step (S50) of obtaining the relational expression F of the molding conditions, the amount of deviation G, and the amount of deformation D, the relational expression F including the learning model L between the amount of deviation G and the amount of deformation D is obtained for the provisional molding condition T. do. Acquisition of the learning model L is performed by the learning model program 242 . Acquisition of the relational expression F is performed by the relational expression acquisition program 244 .

<Setting molding conditions>
A step of setting molding conditions will be described. As shown in FIG. 5, the stage of setting the molding conditions consists of a step (S60) of substituting the molding conditions into the relational expression F to calculate the amount of deviation G and the amount of deformation D; It has a step (S70) of determining whether the difference between the deformation amount D and the deformation amount is minimal, and a step (S80) of setting the optimum molding conditions.

In the step (S60) of substituting the molding conditions into the relational expression F to calculate the deviation amount G and the deformation amount D, a predetermined range of molding conditions is substituted into the relational expression F, and the deviation amount G and the deviation amount in the predetermined range of the molding conditions are calculated. A deformation amount D is calculated. Here, as the temporary molding conditions in the predetermined range to be substituted into the relational expression F, the molding conditions used in the temporary molding conditions T, for example, are used. The deviation amount G and the deformation amount D are calculated by the calculation program 246 .

In the step (S70) of determining whether the divergence amount G is the smallest and whether the difference between the deformation amount D and the target deformation amount is the smallest (S70) and the step of setting the optimum molding conditions (S80), the relational expression F is formed with molding conditions within a predetermined range. When the deviation amount G and the difference between the target deformation amount and the deformation amount D are determined to be the minimum among the deviation amount G and the deformation amount D calculated by substituting This is done by setting conditions (S80). Further, if the deviation amount G and the difference between the target deformation amount and the deformation amount D are not determined to be the minimum (No in S70), the deviation amount G and the deformation amount D are newly calculated for another molding condition. Then, the difference between the amount of divergence G and the amount of deformation D is determined. Here, molding condition determination is performed by the molding condition setting program 248 . The set peen forming conditions R are sent to the peen forming device 300 .

<Pean forming process>
In the stage of the peen forming process, the actual peen forming process is performed on the member to be formed P based on the peen forming conditions R in the actual forming step (S90). The peen forming process is performed by the peen forming device 300, and the shot projection unit 330 shoots the peen forming condition R at the set peen forming condition R on the member to be formed P attached to the peen forming device 300. This is performed by projecting shots at the projection amount and the movement speed of the shot projection unit 330 .

<About anisotropic strain>
The peen forming condition setting method according to the present invention can also be applied to the case where an anisotropic strain is produced in a member to be formed by peen forming. As mentioned above, the peen forming process is, in principle, biaxial forming that causes isotropic strain in the plate-like body. be able to. There are various methods for generating anisotropic strain. For example, a method in which shots are projected from the front and back surfaces of the member to be formed, a method in which the member to be formed is clamped so that the shot surface protrudes, and the member to be formed is pre-stressed There are a method of projecting a shot while a tensile force is generated on the shot surface side, and a method of projecting a shot with a rectangular indenter against the shot surface. In any method, a relational expression F including a learning model L between the provisional molding conditions T and the amount of deviation G and the amount of deformation D is obtained, the amount of deviation G and the amount of deformation D are calculated from the molding conditions, and the optimum molding conditions R are calculated. can be set.

<About anisotropic members>
The relational expression F is not limited to the to-be-shaped member P made of only a plate-like material as shown in FIG. 1, but the to-be-shaped member Q as shown in FIG. The reinforcing member Q2 is arranged on one side of the plate-like member Q1 in parallel with the long side of the plate-like member Q1, and the plate-like member Q1 of the molded member Q and the reinforcing member Q2 are integrated. be able to. Also in this case, as shown in FIG. 7, for the analysis model N of the member to be molded Q, a plurality of individual regions are defined on the front and back surfaces of the plate-like member Q1 and the reinforcing member Q2. By acquiring the relational expression F including the learning model L of the molding conditions on the front and back surfaces of the , and the deviation amount G and the deformation amount D, the optimum molding conditions R are set in the same manner as for the molded member P. can be done.

An example of the case where the relational expression F in the case where the molded member Q has the plate member Q1 and the reinforcing member Q2 is obtained by multiple regression analysis is shown below.
G= _C9 + _C10.T1 + _C11.T2 + _C12.T3 + _C13.T4 (4)
HzX= _C14 + _C15.T1 + _C16.T2 + _C17.T3 + _C18.T4 (5)
HzY= _C19 + _C20.T1 + _C21.T2 + _C22.T3 + _C23.T4 (6)
HxY= _C24 + _C25.T1 + _C26.T2 + _C27.T3 + _C28.T4 (7)
HxZ= _C29 + _C30.T1 + _C31.T2 + _C32.T3 + _C33.T4 (8)
Here, HzX and HzY are arc heights with respect to the AzX-axis and AzY-axis in the Z direction, respectively, and HxY and HxZ are arc heights with respect to the AxY-axis and AxZ-axis in the X direction (not shown), respectively. T3 is the molding condition for the X+ direction surface of the side surface (YZ surface) of the reinforcing member Q2, and T4 is the molding condition for the X- direction surface of the side surface (YZ surface) of the reinforcing member Q2. Also, C ₉ to C ₃₃ are weights obtained by multiple regression analysis or the like. From the relational expression F obtained in this manner, the optimum molding conditions R can be set for the member Q to be molded by the same method as for the member P to be molded.

Below, the effects of the embodiments according to the present invention will be described for each embodiment.
A first embodiment according to the present invention is a method for setting peen forming conditions, in which an analysis model M of a member to be formed P is divided into a plurality of individual regions, and analysis is performed based on predetermined temporary forming conditions T. a step of acquiring the overall temporary forming shape B, a step of acquiring the deviation amount G of the individual region from the reference shape S and the deformation amount D of the individual region for the entire temporary forming shape B, and the provisional forming condition T and a step of obtaining a relational expression F between the amount of deviation G and the amount of deformation D; and setting the temporary forming condition T under which the difference of the deformation amount D from the target deformation amount is the smallest to the optimum peen forming condition R.

According to the first embodiment, from the provisional molding condition T, the overall provisional molding shape B obtained based on the provisional molding condition T, and the divergence amount G and the deformation amount D obtained from the overall provisional molding shape B , a relational expression F between the molding conditions, the amount of divergence, and the amount of deformation. Further, regarding the divergence amount G and the deformation amount D calculated by substituting the molding conditions into the relational expression F, the difference between the divergence amount G and the target deformation amount and the deformation amount D is the optimum peen forming condition R , it is possible to improve the accuracy of the overall shape with respect to the overall target shape of the molding shape.

In a second embodiment of the present invention, in the peen forming condition setting method according to the first embodiment, the reference shape S is configured as a jig face plate, and the deviation amount G is a method of the jig face plate and the forming shape. It is the difference in the linear direction.

According to the second embodiment, the reference shape S is configured as a jig face plate, and the difference in the normal direction between the jig face plate and the forming shape can be obtained as the divergence amount G.

A third embodiment of the present invention is the peen forming condition setting method according to the first embodiment or the second embodiment, wherein the deviation amount G is set in the setting area in which the deviation amount G is set over a plurality of individual areas. This is a peen condition setting method in which the deviation amount G of the individual region is acquired by being set.

According to the third embodiment, since the set region U of the reference shape S is configured corresponding to a plurality of individual regions, the individual region has a divergence amount from the set region U corresponding to the plurality of individual regions. G can be obtained.

A fourth embodiment of the present invention is the peen molding condition setting method according to any one of the first embodiment to the third embodiment, wherein the relational expression F is the obtained provisional molding condition T and the divergence amount G A learning model L generated by performing machine learning using the deformation amount D is included.

According to the fourth embodiment, since the relational expression F includes the molding conditions and the learning model L that has been machine-learned with respect to the deviation amount and the deformation amount in the entire region, the molding conditions are substituted into the relational expression F for calculation. The reliability of the deviation amount G and the deformation amount D can be improved.

A fifth embodiment of the present invention is the peen forming condition setting method according to any one of the first to fourth embodiments, wherein the temporary forming conditions T cause an anisotropic strain in the member P to be formed. Includes molding conditions that cause

According to the fifth embodiment, the peen forming conditions R according to the first embodiment or the fourth embodiment include forming conditions capable of producing an anisotropic strain in the member P to be formed. The same effect as in the first embodiment or the second embodiment can be obtained even when peen forming processing by directional strain is required.

A sixth aspect of the present invention is the pean forming condition setting method according to any one of the first to fifth aspects, wherein the member Q to be formed comprises a plate member Q1 and a reinforcing member Q2. The reinforcing member Q2 is arranged on one side of the plate-like member Q1 along the longitudinal direction of the plate-like member Q1, and the plate-like member Q1 and the reinforcing member Q2 are integrally formed.

According to the sixth embodiment, in any one of the first to fifth embodiments, the member to be molded P is a reinforcing member arranged in the longitudinal direction of the plate-like member Q1 on one side of the plate-like member Q1. The same effects as those of the first to sixth embodiments can be obtained in the member to be molded Q in which Q2 is integrally constructed.

According to a seventh embodiment of the present invention, in the peen forming method according to any one of the first to sixth embodiments, the member Q to be formed is a member used for a wing body of an airplane.

According to a seventh embodiment, the molding methods according to the first to sixth embodiments can be used for aircraft wing bodies.

The peen forming method according to the eighth embodiment of the present invention is based on the peen forming condition R set by the peen forming condition setting method according to any one of the first to seventh embodiments. A molding step for molding the member Q is provided.

According to the eighth embodiment, the pean forming process can be performed under the pean forming conditions R set by the pean forming condition setting method according to the first to seventh embodiments.

A peen forming condition setting device according to a ninth embodiment of the present invention divides an analysis model M of a member P to be formed into a plurality of regions, and performs analysis based on predetermined temporary forming conditions T. For the overall provisional forming shape B, the amount of deviation G of the individual region from the reference shape S and the amount of deformation D of the individual region are obtained, and the relational expression F between the provisional forming condition T, the amount of separation G, and the amount of deformation D is obtained. means for calculating the amount of deviation G and the amount of deformation D by substituting a predetermined temporary molding condition T into the relational expression F; and means for setting the temporary molding condition T under which the difference between the deformation amount D and the target deformation amount is the smallest as the optimum molding condition.

According to the ninth embodiment, the pean forming condition setting device 200 can achieve the same effect as the first embodiment.

100... Peen forming system, 200... Peen forming condition setting device,
210... Arithmetic unit, 220... Input/output unit, 230... Recording unit,
232... area division program, 234... provisional molding condition setting program,
236... Shape acquisition program, 238... Deviation amount acquisition program,
240 ... deformation amount acquisition program, 242 ... learning model program,
244 ... relational expression acquisition program, 246 ... calculation program,
248 ... molding condition setting program, 250 ... molding result database,
300... Peen forming device, 310... Control unit, 320... Input/output unit,
330... shot projection unit, 340... recording unit,
A... Individual region, B... Whole temporary molding shape, D... Deformation amount, F... Relational expression,
G... deviation amount, H... target molding shape, K... correct answer data, M... analysis model,
P, Q... molded member, Q1... plate-shaped member, Q2... reinforcing member,
R... Peen molding conditions, optimum molding conditions, S... Reference shape, T... Temporary molding conditions,
U: setting area

Claims (9)

obtaining a provisional formed shape by dividing the analysis model of the member to be formed into a plurality of individual regions and performing analysis based on predetermined provisional forming conditions;
a step of acquiring the deviation amount of the individual region from the reference shape and the deformation amount of the individual region for the temporary forming shape;
a step of acquiring a relational expression among the temporary molding conditions, the amount of divergence, and the amount of deformation;
Among the deviation amount and the deformation amount calculated by substituting the temporary molding condition into the relational expression, the deviation amount is the smallest, and the difference between the deformation amount and the target deformation amount is the smallest. setting molding conditions to optimum molding conditions;
A method for setting peen molding conditions. In the peen forming condition setting method according to claim 1,
The peen forming condition setting method, wherein the reference shape is configured as a jig faceplate, and the divergence amount is a difference in the normal direction between the jig faceplate and the forming shape. In the peen forming condition setting method according to claim 1 or claim 2,
A peen forming condition setting method, wherein the amount of deviation is set in a setting area for setting the amount of deviation over a plurality of the individual areas, so that the amount of deviation of the individual area is acquired. In the peen forming condition setting method according to any one of claims 1 to 3,
The peen forming condition setting method, wherein the relational expression includes a learning model generated by performing machine learning using the obtained temporary forming conditions, the divergence amount, and the deformation amount. In the peen forming condition setting method according to any one of claims 1 to 4,
The temporary molding conditions include a peen molding condition setting method including molding conditions that cause anisotropic strain. The pean forming condition setting method according to any one of claims 1 to 5,
The member to be molded has a plate-like member and a reinforcing member,
The reinforcing member is arranged on one side of the member to be molded along the longitudinal direction of the plate member,
The peen forming condition setting method, wherein the plate member and the reinforcing member are integrally formed. In the peen forming condition setting method according to any one of claims 1 to 6,
The method for setting peen forming conditions, wherein the member to be formed is a member used for a wing body of an airplane. A peen forming method for a plate-like member,
A peen forming method for forming the member to be formed based on optimum forming conditions set by the peen forming condition setting method according to any one of claims 1 to 7. The analysis model of the member to be formed is partitioned into a plurality of regions, and the provisional formed shape obtained by executing the analysis based on the predetermined provisional forming conditions is divided into the amount of deviation of the individual region from the reference shape, and the amount of deviation of the individual region. means for obtaining a deformation amount and obtaining a relational expression between the temporary molding condition, the divergence amount, and the deformation amount;
means for calculating the divergence amount and the deformation amount by substituting the predetermined temporary molding condition into the relational expression;
means for setting the provisional molding condition under which the deviation amount is the smallest among the calculated deviation amount and the deformation amount and the difference of the deformation amount from the target deformation amount is the smallest, as an optimum molding condition;
A peen forming condition setting device.

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