WO2021070395A1 - ピーン成形条件設定方法、ピーン成形方法およびピーン成形条件設定装置 - Google Patents

ピーン成形条件設定方法、ピーン成形方法およびピーン成形条件設定装置 Download PDF

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WO2021070395A1
WO2021070395A1 PCT/JP2019/040339 JP2019040339W WO2021070395A1 WO 2021070395 A1 WO2021070395 A1 WO 2021070395A1 JP 2019040339 W JP2019040339 W JP 2019040339W WO 2021070395 A1 WO2021070395 A1 WO 2021070395A1
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WIPO (PCT)
Prior art keywords
molding
peen
amount
molding condition
condition setting
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PCT/JP2019/040339
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English (en)
French (fr)
Japanese (ja)
Inventor
大河 岡井
亮 河野
山田　毅
貴史 小▲崎▼
宏輔 赤沼
敏宏 ▲桑▼野
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三菱重工業株式会社
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Application filed by 三菱重工業株式会社 filed Critical 三菱重工業株式会社
Priority to JP2021551102A priority Critical patent/JP7178508B2/ja
Priority to PCT/JP2019/040339 priority patent/WO2021070395A1/ja
Publication of WO2021070395A1 publication Critical patent/WO2021070395A1/ja

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21D—WORKING OR PROCESSING OF SHEET METAL OR METAL TUBES, RODS OR PROFILES WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21D31/00—Other methods for working sheet metal, metal tubes, metal profiles
    • B21D31/06—Deforming sheet metal, tubes or profiles by sequential impacts, e.g. hammering, beating, peen forming
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B24—GRINDING; POLISHING
    • B24C—ABRASIVE OR RELATED BLASTING WITH PARTICULATE MATERIAL
    • B24C1/00—Methods for use of abrasive blasting for producing particular effects; Use of auxiliary equipment in connection with such methods
    • B24C1/10—Methods for use of abrasive blasting for producing particular effects; Use of auxiliary equipment in connection with such methods for compacting surfaces, e.g. shot-peening

Definitions

  • the present invention relates to a peen forming condition setting method, a peen forming method, and a peen forming condition setting device.
  • the plate peen molding conditions have been set by using the amount of deformation of a plurality of regions partitioned on the surface of the member to be molded as an index before and after molding.
  • Patent Document 1 in the peen forming method, a machining area dividing step of dividing a metal member into a plurality of regions and obtaining a plate thickness and a curvature to be formed for each region, and a plurality of test pieces having different plate thicknesses are described.
  • the projection condition data summarizing the projection conditions of the shot for giving a predetermined curvature
  • the projection conditions corresponding to the combination of the plate thickness and the curvature to be formed for each region are obtained, and based on the obtained projection conditions.
  • An invention comprising a molding step of molding each region is disclosed.
  • 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 amount of deformation in one region when molding is performed under the temporary molding conditions affects the amount of deformation in the other region.
  • An invention is disclosed in which the degree of influence is calculated for each region as relational information, and peen forming conditions in one region and the other region for achieving a deformation amount to be a target shape are set based on the relational information.
  • the projection conditions optimized for the combination of the plate thickness and the curvature for each region are used as they are as the projection conditions for each region in the entire molding.
  • the peen forming method according to the description of Patent Document 2 only the amount of deformation for each region for forming the member into a shape after molding is used as an index. As a result, the molding accuracy may be lowered in the molded shape where the precision is particularly required.
  • the analysis model of the member to be formed is divided into a plurality of individual regions, and the analysis is executed based on the predetermined temporary forming conditions to obtain the temporarily formed shape.
  • the 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 molding shape, the temporary molding condition, the deviation amount, and the deformation amount is the smallest and the deformation amount with respect to the target deformation amount. It has a step of setting the temporary molding condition in which the difference between the two is the minimum to the optimum molding condition.
  • the molding method according to the present disclosure for achieving the above object includes a step of peen molding the member to be molded under the optimum molding conditions set by the peen molding condition setting method.
  • Pean forming condition setting device for setting peen forming conditions.
  • Temporary forming shape obtained by dividing an analysis model of a member to be formed into a plurality of regions and executing analysis based on predetermined temporary forming conditions.
  • the divergence amount is the smallest and with respect to the target deformation amount. It has means for setting the temporary molding condition in which the difference in the amount of deformation is the minimum to the optimum molding condition.
  • the molding conditions that minimize the deviation amount from the reference shape and the difference between the target deformation amount and the deformation amount of the molding shape by the peen molding process are set as the optimum molding conditions, the molding is performed under the optimum molding conditions. It is possible to improve the accuracy of the formed molded shape as a whole with respect to the target molded shape.
  • FIG. 1 is a diagram showing an analysis model partitioned 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 molding system including a molding condition setting device according to an embodiment of the present invention.
  • FIG. 3 is a diagram showing the amount of deviation in the molded shape of the member to be molded according to the embodiment of the present invention.
  • FIG. 4 is a diagram showing an arc height which is the amount of deformation in the molded shape of the member to be molded according to the embodiment of the present invention.
  • FIG. 5 is a flowchart showing a peen forming condition setting method according to an embodiment of the present invention.
  • FIG. 6 is a diagram showing a member to be molded provided 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 member to be molded including a reinforcing member according to an embodiment of the present invention is divided into a plurality of regions.
  • Pean forming is a process in which a plurality of small granular steel balls are projected onto the surface of a plate-shaped member or the like, and the pressure causes plastic deformation from the surface side of the plate-shaped member to cause bending deformation of the plate-shaped member.
  • the peen-processed plate-shaped member has a so-called isotropic strain that produces a curved shape such that the processed surface side is projected around the processing center.
  • the molding conditions used for the peen forming process include, for example, the speed at which the steel ball is projected, the amount of projection per hour, and the moving speed of the shot projection unit that projects the steel ball. The molding shape can be adjusted.
  • the member P to be molded according to the present invention is a long plate-shaped member, and the material is, for example, a metal containing an aluminum alloy.
  • the member P to be molded according to this embodiment is used for parts constituting the wing body of an airplane.
  • the peen forming system 100 includes a peen forming condition setting device 200 for setting the peen forming condition R and a peen forming apparatus 300 for actually performing the peen forming process.
  • the peen forming condition setting device 200 sets the peen forming condition R for peen forming the member P to be formed
  • the peen forming device 300 sets the peen forming condition R based on the peen forming condition R.
  • a peen forming process is performed on the member P, and a forming shape is acquired.
  • the peen forming condition R is composed of a projection speed for projecting a steel ball, a projection amount per hour, and a moving speed of the shot projection unit 350 described later with respect to the member P to be formed.
  • the peen forming condition R is not limited to these configurations.
  • the peen forming condition setting device 200 and the peen forming apparatus 300 constituting the peen forming system 100 will be described with reference to FIG.
  • the peen forming condition setting device 200 and the peen forming device 300 may be integrally configured.
  • the peen forming condition setting device 200 includes an arithmetic unit 210 that performs arithmetic processing, an input / output unit 220 that performs input / output processing with the outside, and a recording unit 230 that performs input / output processing of programs and data. I have.
  • the recording unit 230 and the input / output unit 220 are connected to the calculation unit 210, respectively.
  • the peen forming condition setting device 200 is configured as a computer terminal, and the input / output unit 220 may include, for example, a keyboard, a mouse, and a display monitor as external input devices.
  • the calculation unit 210 performs calculation 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 peen forming condition setting device 200 in general.
  • the calculation unit 210 is configured as a CPU.
  • the recording unit 230 records the program and data. As shown in FIG. 2, the recording unit 230 includes an area partition program 232, a temporary molding condition setting program 234, a shape acquisition program 236, a deviation 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 are provided. Further, the recording unit 230 includes a molding result database 250.
  • the recording unit 230 is composed of a memory and an HDD.
  • the region division program 232 partitions a plurality of regions on the front surface and the back surface of the analysis model M based on the input analysis model M and target molding shape H of the member P to be molded.
  • the analysis model M is the shape of the member P to be molded before molding, and is given by, for example, CAD data.
  • the analytical model M has information about the material.
  • the target forming shape H is a model of the shape to be obtained by the peen forming process, and is given by, for example, CAD data.
  • the partitioning of the plurality of regions to the analysis model M is performed according to, for example, the material, the plate thickness, and the curvature, and the partitioning of the plurality of regions to the analysis model M is often performed, for example, for a portion where the shape or curvature changes significantly. Area is partitioned, and a small area may be partitioned for a portion where the change in shape or curvature is small.
  • the temporary molding condition setting program 234 sets a plurality of temporary molding conditions T.
  • the temporary forming condition T is composed of a projection speed for projecting a steel ball, a projection amount per hour, and a moving speed of the shot projection unit 330.
  • the temporary molding condition T is not limited to these configurations.
  • the shape acquisition program 236 simulates the peen molding process based on the input analysis model and molding conditions, and outputs the molding shape (temporary molding shape). That is, when the analysis model M and the temporary molding condition T are input, the shape acquisition program 236 outputs the entire temporary molding shape B which is the molding shape for the analysis model M.
  • Examples of the method used in the simulation of the peen forming process in the shape acquisition program 236 include the finite element method.
  • the deviation amount acquisition program 238 acquires the deviation amount G of the individual region A in the molding shape with respect to the setting region U of the reference shape S.
  • the reference shape S has a surface shape after molding, and the reference shape S is divided into a setting region U.
  • the setting area U of the reference shape S is configured to include a plurality of individual areas.
  • the deviation amount G means the distance of the center point AC of the individual region A of the molded shape in the plane direction with respect to the set region U of the reference shape S.
  • the reference shape S may be a mold shape with respect to the molding shape.
  • the reference shape S is created by, for example, CAD data.
  • the deformation amount acquisition program 240 acquires the deformation amount D of each individual region before and after molding the analysis model M.
  • 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.
  • the deformation amount D refers to the arc height in each individual region.
  • the arc height can be obtained from the shape after molding. As shown in FIG. 4, the arc height is the midpoint between the center point AC of the surface of the individual region A in the shape after molding and the two sides DX1 and DX2 parallel to the X-axis direction of the sides constituting the individual region A.
  • the deformation amount D may be any amount as long as the individual region A represents deformation due to the peen forming process, and is not necessarily limited to the arc height.
  • the learning model program 242 obtains a learning model L that derives the deviation amount G and the deformation amount D from the molding conditions from the data group of the molding conditions, the deviation amount G, and the deformation amount D.
  • the learning model L is included in the relational expression F described later.
  • the learning model L is a weighted function for calculating the deviation amount G and the deformation amount D, which are the objective variables, from the molding conditions, which are the explanatory variables.
  • the learning model L is, for example, a weighted function for calculating the deviation amount G and the deformation amount D from the temporary molding condition T in the overall temporary molding shape B.
  • the learning model L is acquired as, for example, a neural network.
  • the learning model L may acquire the correct answer data K including the molding conditions, the deviation amount G, the deformation amount D, and the molding quality result J as the learning model L after machine learning as so-called supervised learning.
  • the learning model L can calculate highly accurate data by correcting the weights so that the deviation amount G and the deformation amount D calculated from the given molding conditions are correct. it can.
  • the molding result data includes the actual peen molding condition R, the deviation amount G from the reference shape S, the deformation amount D, and the molding quality result J, and can be used as so-called correct answer data K. it can.
  • the learning model program 242 can use the molding result data recorded in the molding result database 250 described later.
  • the relational expression acquisition program 244 acquires the relational expression F from the data group of the molding conditions, the deviation amount G, and the deformation amount D.
  • the correlation is obtained by using the dissociation amount G and the deformation amount D as the objective variable and the molding shape as the explanatory variable.
  • the relational expression F is obtained by, for example, multiple regression analysis of the data groups of the molding conditions, the dissociation amount G, and the deformation amount D.
  • the relational expression acquisition program 244 can use the molding result data recorded in the molding result database 250 described later.
  • the calculation program 246 substitutes the molding conditions in a predetermined range into the relational expression F, and calculates the deviation amount G and the deformation amount D.
  • the relational expression F includes the learning model L
  • the divergence amount G and the deformation amount D are calculated by substituting the molding conditions into the relational expression F in the following description.
  • the case of substituting the molding conditions into the learning model L to acquire the deviation amount G and the deformation amount D is also included.
  • the molding condition setting program 248 determines whether the deviation amount G calculated by the calculation program 246 and the difference between the target deformation amount and the deformation amount D are the minimum. When a plurality of deviation amounts G and deformation amounts D exist, the molding conditions when the difference between the deviation amount G and the target deformation amount and the deformation amount D is the smallest are set as the optimum peen molding conditions R. Set.
  • the molding condition setting program 248 may be given a threshold value for determining that it is the minimum. In this case, the molding condition setting program 248 can determine that the difference between the deviation amount G and the target deformation amount and the deformation amount D is equal to or less than the threshold value, and the molding condition in this case is the optimum peen. It is set as the molding condition R.
  • the molding result database 250 has information on the member to be molded that has actually been peen-molded, material, molding shape, molding conditions, and good / bad data of the machining result.
  • the molding result database 250 may be configured so that the molding result in the peen molding apparatus 300 is input to the peen molding condition setting apparatus 200 via the input / output unit 220 and recorded in the molding result database 250.
  • the input / output unit 220 performs data input / output processing with the outside of the peen forming condition setting device 200 based on an instruction from the calculation unit 210. As shown in FIG. 2, the input / output unit 220 is connected to the input / output unit 320 of the peen forming apparatus 300. The input / output unit 220 is wiredly connected to a LAN (not shown). Further, the input / output unit 220 may be wirelessly connected by WiFi or the like to input / output data, and may further input / output data via a portable recording medium such as a USB memory.
  • the peen forming apparatus 300 actually performs peen forming based on the peen forming condition R set for the member P to be formed.
  • the peen forming apparatus 300 includes a control unit 310, an input / output unit 320, a shot projection unit 330, and a recording unit 340.
  • the peen forming process in this embodiment is performed by moving the shot projection unit 330 with respect to the member P to be formed attached to the peen forming apparatus 300 at a predetermined moving speed.
  • the peen forming process may be performed by moving the member P to be formed with respect to the shot projection unit 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 general control regarding the peen forming apparatus 300.
  • the control unit 310 includes a CPU, a memory, and an HDD (not shown). Further, the control unit 310 may include a display monitor that displays the state of the peen forming process.
  • the input / output unit 320 receives the peen forming condition R from the peen forming condition setting device 200, and also performs data input / output processing with the outside.
  • the input / output unit 320 is wiredly connected to a LAN (not shown). Further, 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 the peen forming process on the plate-shaped member based on the peen forming condition R instructed by the control unit 310.
  • the shot projection unit 330 is configured to be capable of shot projecting a steel ball based on the peen forming condition R, and the projection speed of the steel ball, the projection amount per hour, and the moving speed of the shot projection unit 330 can be adjusted. ing.
  • the moving speed is the moving speed of the member P to be molded.
  • the molding method of the member P to be molded will be described with reference to the flowchart shown in FIG.
  • 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 condition (S10 to S50), and the second step is the step of calculating the peen molding condition R from the relational expression F. (S60 to S80), the third step is the step (S90) of actually performing the peen forming process on the plate-shaped member based on the calculated peen forming condition R.
  • each stage will be described in detail.
  • the step of acquiring the relational expression F includes a step (S10) of partitioning a plurality of regions in the analysis model M of the member P to be molded and a step (S20) of setting a temporary molding condition T.
  • It has a step (S50) of acquiring the relational expression F of the quantity G and the deformation quantity D.
  • the front surface and the back surface of the analysis model M of the member P to be molded are displayed on the front surface and the back surface of the analysis model M of the member P to be molded based on the input members P to be molded and the target molding shape H. Divide into multiple areas.
  • the partitioning of the analysis model M into a plurality of regions is performed by the region partition program 232.
  • the temporary forming condition T is set for the analysis model M in which the region is divided.
  • the temporary molding condition T is acquired by the temporary molding condition setting program 234.
  • the entire analysis model M is simulated by numerical analysis based on the plurality of temporary forming conditions T, and a plurality of overall temporary forming shapes B are acquired. ..
  • the acquisition of the entire temporary molding shape B is performed by the shape acquisition program 236.
  • the temporary molding condition T, the deviation amount G from the reference shape S, and the obtained total temporary molding shape B are obtained.
  • the amount of deformation D is acquired.
  • the dissociation amount G is acquired by the dissociation amount acquisition program 238, and the deformation amount D is acquired by the deformation amount acquisition program 240.
  • the relational expression F including the learning model L with the respective deviation amount G and the deformation amount D is acquired for the temporary molding condition T.
  • the acquisition of the learning model L is performed by the learning model program 242.
  • the acquisition of the relational expression F is performed by the relational expression acquisition program 244.
  • the steps of setting the molding conditions include the step (S60) of substituting the molding conditions into the relational expression F to calculate the deviation amount G and the deformation amount D, the minimum deviation amount G, and the target. It includes a step (S70) for determining whether the difference in the deformation amount D with respect to the deformation amount is the minimum, and a step (S80) for setting the optimum molding conditions.
  • the molding conditions in a predetermined range are substituted into the relational expression F, and the deviation amount G and the deviation amount G in the molding conditions in the predetermined range
  • the amount of deformation D is calculated.
  • the temporary molding conditions in a predetermined range assigned to the relational expression F for example, the molding conditions used in the temporary molding condition T are used.
  • the deviation amount G and the deformation amount D are calculated by the calculation program 246.
  • the molding conditions in a predetermined range are set in the relational expression F.
  • the optimum molding condition is the case where the difference between the deviation amount G and the target deformation amount and the deformation amount D is determined to be the minimum (Yes in S70). It is performed by setting it as a condition (S80). If it is not determined that the difference between the deviation amount G and the target deformation amount and the deformation amount D is the minimum (No in S70), the deviation amount G and the deformation amount D are newly calculated for another molding condition. Therefore, the difference between the deviation amount G and the deformation amount D is determined.
  • the molding condition is determined by the molding condition setting program 248.
  • the set peen forming condition R is sent to the peen forming apparatus 300.
  • the actual peen forming process is performed on the member P to be formed based on the peen forming condition R.
  • the peen forming process is performed by the peen forming apparatus 300, and the projection speed of the peen forming condition R set by the shot projection unit 330 with respect to the member P to be formed attached to the peen forming apparatus 300, per hour. This is performed by projecting a shot at the projection amount and the moving speed of the shot projection unit 330.
  • the peen forming condition setting method according to the present invention can be applied even when anisotropic strain is generated in the member to be formed by the peen forming process.
  • the peen forming process is, in principle, biaxially forming that causes isotropic strain in the plate-like body, and anisotropic strain is particularly forming into a shape that is curved only in one axial direction. be able to.
  • anisotropic strain There are various methods for generating anisotropic strain. For example, a method of projecting shots from the front surface and the back surface of the member to be molded, a method of clamping the member to be molded so that the shot surface protrudes, and a method of preliminarily forming the member to be molded.
  • the relational expression F including the learning model L between the temporary molding condition T, the dissociation amount G, and the deformation amount D is acquired, the dissociation amount G and the deformation amount D are calculated from the molding conditions, and the optimum molding condition R is set. Can be set.
  • the relational expression F is not limited to the plate-shaped member P made of only the plate-shaped material as shown in FIG. 1, and as shown in FIG. 6, the molded member Q includes the plate-shaped member Q1 and the reinforcing member Q2. This also applies when the reinforcing member Q2 is arranged on one side of the plate-shaped member Q1 in parallel with the long side of the plate-shaped member Q1 and the plate-shaped member Q1 and the reinforcing member Q2 of the member Q to be molded have an integral structure. be able to. Also in this case, as shown in FIG.
  • T3 is a molding condition on the side surface (YZ surface) of the reinforcing member Q2 in the X + direction
  • T4 is a molding condition on the side surface (YZ surface) of the reinforcing member Q2 in the X ⁇ direction.
  • C 9 to C 33 are weights obtained by multiple regression analysis or the like. From the relational expression F thus obtained, the optimum molding condition R can be set for the member Q to be molded by the same method as in the case of the member P to be molded.
  • the first embodiment according to the present invention is a peen forming condition setting method, in which an analysis model M of a member P to be formed is divided into a plurality of individual regions, and analysis is executed based on a predetermined temporary forming condition T.
  • the step of acquiring the total temporary molding shape B the 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 total temporary molding shape B, and the temporary molding condition T.
  • the deviation amount G is the smallest.
  • the step of setting the temporary molding condition T in which the difference between the deformation amount D with respect to the target deformation amount is the minimum is set to the optimum peen molding condition R.
  • the optimum peen molding condition R is the one in which the difference between the deviation amount G and the target deformation amount and the deformation amount D is the smallest. Therefore, the accuracy of the entire shape with respect to the overall target shape of the molded shape can be improved.
  • the reference shape S is configured as a jig face plate
  • the deviation amount G is a method of the jig face plate and the molding shape. The difference in the linear direction.
  • the reference shape S is configured as a jig face plate, and the difference between the jig face plate and the molded shape in the normal direction can be acquired as the deviation amount G.
  • the deviation amount G is set in the setting region for setting the deviation amount G over a plurality of individual regions.
  • This is a peen condition setting method in which the deviation amount G of the individual regions is acquired by being set.
  • the individual area since the setting area U of the reference shape S is configured corresponding to a plurality of individual areas, the individual area has a deviation amount with respect to the setting area U corresponding to the plurality of individual areas. G can be obtained.
  • the relational expression F is the acquired temporary molding condition T and the deviation amount G.
  • the learning model L generated by performing machine learning using the deformation amount D is included.
  • the relational expression F since the relational expression F includes the molding condition and the learning model L machine-learned about the deviation amount and the deformation amount in the entire region, the relational expression F is calculated by substituting the molding condition into the relational expression F. The reliability of the deviation amount G and the deformation amount D can be improved.
  • the provisional forming condition T causes anisotropic strain in the member P to be formed. Includes molding conditions to be made.
  • the peen molding condition R according to the first embodiment or the fourth embodiment is different because it includes a molding condition capable of causing anisotropic strain in the member P to be molded. Even when the peen forming process due to the anisotropic strain is required, the same effect as that of the first embodiment or the second embodiment can be obtained.
  • the member Q to be molded in the peen forming condition setting method according to any one of the first to fifth embodiments, includes a plate-shaped member Q1 and a reinforcing member Q2.
  • the reinforcing member Q2 is arranged on one side of the plate-shaped member Q1 along the longitudinal direction of the plate-shaped member Q1, and the plate-shaped member Q1 and the reinforcing member Q2 are integrally formed.
  • the member P to be molded is a reinforcing member arranged in the longitudinal direction of the plate-shaped member Q1 on one side of the plate-shaped member Q1. Even in the member Q to be molded in which Q2 is integrally structured, the same effect as that of the first to sixth embodiments can be obtained.
  • the member Q to be molded is a member used for the wing body of an airplane.
  • the molding method according to the first to sixth embodiments can be used for the wing body of an airplane.
  • the peen molding method according to the eighth embodiment of the present invention is to be molded based on the peen molding condition R set by the peen molding condition setting method according to any one of the first to seventh embodiments. It has a molding step of molding the member Q.
  • the peen forming process can be performed under the peen forming condition R set by the peen forming condition setting method according to the first to seventh embodiments.
  • the peen molding condition setting device is acquired by partitioning the analysis model M of the member P to be molded into a plurality of regions and executing the analysis based on the predetermined temporary molding condition T.
  • the deviation amount G of the individual region and the deformation amount D of the individual region with respect to the reference shape S are acquired, and the relational expression F between the temporary molding condition T, the deviation amount G, and the deformation amount D is acquired.
  • the deviation amount G is the smallest of the means for calculating the deviation amount G and the deformation amount D by substituting the predetermined temporary molding condition T into the relational expression F, and the calculated deviation amount G and the deformation amount D. It also has means for setting the temporary molding condition T, which has the smallest difference in the deformation amount D with respect to the target deformation amount, as the optimum molding condition.
  • the peen molding condition setting device 200 can achieve the same effect as that of the first embodiment.

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PCT/JP2019/040339 2019-10-11 2019-10-11 ピーン成形条件設定方法、ピーン成形方法およびピーン成形条件設定装置 WO2021070395A1 (ja)

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PCT/JP2019/040339 WO2021070395A1 (ja) 2019-10-11 2019-10-11 ピーン成形条件設定方法、ピーン成形方法およびピーン成形条件設定装置

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Cited By (2)

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Publication number Priority date Publication date Assignee Title
CN115090751A (zh) * 2022-07-14 2022-09-23 中国航空制造技术研究院 一种提高带筋整体壁板喷丸成形极限的方法
CN120023586A (zh) * 2025-04-24 2025-05-23 成都国营锦江机器厂 发动机薄壁回转件非焊接区的多处微区域变形校正方法

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JPS56146672A (en) * 1980-01-21 1981-11-14 Boeing Co Method of providing sheet metal part with composite contour
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CN115090751A (zh) * 2022-07-14 2022-09-23 中国航空制造技术研究院 一种提高带筋整体壁板喷丸成形极限的方法
CN115090751B (zh) * 2022-07-14 2024-04-05 中国航空制造技术研究院 一种提高带筋整体壁板喷丸成形极限的方法
CN120023586A (zh) * 2025-04-24 2025-05-23 成都国营锦江机器厂 发动机薄壁回转件非焊接区的多处微区域变形校正方法

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