CN115618669A - Method for predicting shot blasting intensity of shot blasting strengthening process - Google Patents
Method for predicting shot blasting intensity of shot blasting strengthening process Download PDFInfo
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Abstract
The invention provides a method for predicting the shot peening intensity of a shot peening strengthening process, which comprises the steps of establishing a millimeter-scale micro model and a millimeter-scale macro model of a standard Almen test piece, and carrying out shot peening strengthening simulation calculation on the micro model based on preset shot peening strengthening process parameters so as to obtain residual stress on the micro model along the thickness direction; mapping the obtained residual stress into a macroscopic model to serve as a boundary condition, and obtaining a residual stress model on the macroscopic model along the thickness direction through simulation calculation; carrying out an actual shot peening test on the standard Almen test piece according to preset shot peening process parameters to obtain actual residual stress data along the thickness direction; and then, the corrected residual stress model is used for predicting the shot peening intensity of workpieces with different thicknesses under preset shot peening process parameters.
Description
Technical Field
The present invention relates to a shot peening process for peening a surface of a material, and more particularly, to a method for predicting shot strength of a shot peening process.
Background
The shot peening strengthening process is an effective surface strengthening technology, and can spray shot flow through a spray gun, so that the sprayed shot flow can scatter and impact the surface of the metal part, and a corresponding residual stress field is generated on the metal part, thereby realizing the effects of strengthening the metal part and strengthening the fatigue resistance. The shot peening strengthening process has the advantages of simple operation process and obvious effect, and is widely applied to various fields of aerospace, motorcycles, nuclear power, automobiles and the like.
In the actual production process, the process file generally specifies the strengthening degree of shot peening required by different parts and different structures. However, there are many factors that affect the shot peening effect of the shot peening process, such as the properties of the material itself, such as density, poisson's ratio, young's modulus, and the effects of various shot peening process parameters. Meanwhile, the operation of testing the shot blasting process parameters one by one to obtain the target shot blasting strengthening effect by utilizing the shot blasting machine is complex, a large amount of manpower and material resources are consumed, a large amount of experimental data is compared to obtain reasonable shot blasting parameters, and the target shot blasting strengthening effect is difficult to obtain simply, quickly and efficiently.
The existing numerical simulation method can perform corresponding simulation calculation on the shot peening process, so that a large amount of simulated stress strain is obtained, and the shot peening process is further researched. Moreover, the existing numerical simulation software can establish shot peening strengthening simulation models of single shots and array shots and also establish a random shot model to simulate the real shot peening strengthening process for simulation calculation, so that the authenticity of a simulation result is greatly improved. However, as the simulation model approaches the real shot peening simulation process, the amount of calculation for performing simulation calculation increases dramatically, so that the cost for performing shot peening effect simulation calculation of the shot peening process rises dramatically, and the configuration for performing accurate and effective shot peening effect simulation calculation needs to be high and is expensive.
In addition, most of organizations or organizations for researching the peening process of the peening process are domestic and foreign colleges and research organizations, and the simulation result is corrected by lacking of industry practice knowledge and engineering experiment data, so that the simulation result often deviates from the actually achieved peening effect, the simulation result is not credible, the peening effect cannot be reliably predicted by the existing method, and the phenomenon that the peening process parameters cannot be recommended cannot be guided.
Disclosure of Invention
Therefore, the invention provides a novel shot peening strength prediction method for shot peening strengthening process, which aims to solve the technical problems that the simulation calculation amount of the existing shot peening strengthening process is too large, the deviation between the simulation result and the actual engineering result is too large, the existing simulation result cannot accurately and reliably predict the shot peening strengthening effect, and the parameter recommendation of the shot peening strengthening process cannot be carried out aiming at the given shot peening strengthening effect.
Specifically, the invention provides a prediction method of shot peening intensity for shot peening process, which comprises the following steps:
establishing a millimeter-scale microscopic model of a standard Almen test piece, and carrying out shot peening simulation calculation on the microscopic model based on preset shot peening process parameters to obtain residual stress on the microscopic model along the thickness direction;
establishing a macroscopic model of a standard Almen test piece, mapping the residual stress of the obtained microscopic model into the macroscopic model to be used as a boundary condition, and obtaining a residual stress model on the macroscopic model along the thickness direction through simulation calculation;
carrying out an actual shot peening test on the standard Almen test piece according to a preset shot peening process parameter so as to obtain actual residual stress data along the thickness direction on the standard Almen test piece;
and then, the corrected residual stress model is used for predicting the shot peening intensity of workpieces with different thicknesses under preset shot peening process parameters.
According to one embodiment of the invention, the prediction method further comprises using a residual stress model of the macro model to predict shot peening strengths for shot peening workpieces of different thicknesses under predetermined peening process parameters.
According to another embodiment of the invention, the shot peening process parameters include one or more of: shot material, shot size, shot velocity, shot jet direction, shot flow rate, shot pressure, shot distance, and shot angle.
According to another embodiment of the present invention, the prediction method further comprises changing the value of one or more of the shot peening process parameters a plurality of times to obtain a corrected residual stress model under different shot peening process parameters; and obtaining a relation curve between the shot peening process parameters and the shot peening intensity of the workpieces with the same thickness and establishing a corresponding parameter intensity relation model.
According to another embodiment of the invention, the prediction method further comprises recommending one or more values of the shot peening process parameter based on the target shot peening intensity value of the user according to the parameter intensity relation model and the relation curve between the shot peening process parameter and the shot peening intensity.
According to another embodiment of the present invention, the prediction method further includes encapsulating both the corrected residual stress model and the parameter strength relationship model in application software.
According to another embodiment of the present invention, the step of obtaining the residual stress in the thickness direction on the micro model includes establishing a millimeter-scale micro model based on finite element software, and performing the shot peening strengthening simulation calculation on the micro model by a finite element calculation method to obtain the residual stress distribution on the micro model.
According to another embodiment of the present invention, the step of obtaining the residual stress in the thickness direction on the micro model further comprises extracting the residual stress in the thickness direction on the micro model by a statistical knowledge method according to the residual stress distribution of the micro model.
According to another embodiment of the present invention, the step of obtaining actual residual stress data comprises acquiring an image of a standard Almen's test piece surface subjected to shot peening test based on machine vision technology, and identifying shot of the standard Almen's test piece surface to statistically calculate shot coverage; and combining the peening parameters including the peening material, the peening size and the peening speed with the peening coverage rate to obtain actual residual stress data in the thickness direction on the standard Almen test piece.
According to another embodiment of the present invention, the step of obtaining actual residual stress data comprises detecting the residual stress on the surface of the standard alder gate test piece subjected to shot peening test by using a residual stress detection device or an electrolytic corrosion delamination method, and obtaining actual residual stress data on the standard alder gate test piece along the thickness direction.
According to another embodiment of the invention, the step of using the residual stress model to predict shot peening comprises the steps of obtaining residual stress data of a workpiece with a specific thickness along the thickness direction according to the residual stress model, calculating an arc height value corresponding to the residual stress data by using a general arc height test method, and obtaining the shot strength of the corresponding shot peening process according to the calculated arc height value.
According to another embodiment of the present invention, the standard Almen test strip includes a standard Almen test strip A, a standard Almen test strip C and a standard Almen test strip N.
On the basis of the common knowledge in the field, the above preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the above embodiments of the present invention are: the residual stress model generated by shot peening is established through the microscopic simulation model, and then the microscopic simulation model is mapped to the macroscopic simulation model, so that the simulation calculation time is shorter, the precision is higher, and the calculation cost of the shot peening process simulation analysis is effectively reduced. And the actual experimental data is used for carrying out reverse iteration optimization simulation on the obtained residual stress model, so that the prediction result of the corrected residual stress model is matched with the engineering practice result. In addition, the method also extracts a shot peening mechanism and packages the shot peening mechanism into software, so that accurate prediction of shot peening and accurate recommendation of process parameters are facilitated.
Drawings
FIG. 1 is a flowchart of a method for predicting shot strength for a shot peening process according to a preferred embodiment of the present invention;
FIG. 2 is a microscopic model constructed according to the prediction method of FIG. 1;
FIG. 3 is a schematic diagram of the simulation results of shot peening simulation calculations performed on the micro model according to the prediction method of FIG. 1;
fig. 4 is a schematic diagram of a residual stress model of the macro model in the thickness direction obtained according to the prediction method in fig. 1.
Detailed Description
To make the objects, technical solutions and advantages of the present application clearer, technical solutions in embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings showing a plurality of embodiments according to the present application. It should be understood that the described embodiments are only a few embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed in the present application without undue experimentation, shall fall within the scope of protection of the present application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising," "having," and the like in the description and claims of this application and in the description of the foregoing figures are open-ended terms. Thus, a method that "comprises," "has," such as one or more steps, has one or more steps, but is not limited to having only those one or more steps.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by a person skilled in the art that the embodiments described herein can be combined with other embodiments.
The term "and/or" in this application is only one kind of association relationship describing the associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in this application generally indicates that the preceding and following associated objects are in an "or" relationship.
The shot peening strengthening process is an effective surface strengthening technology, and can spray shot flow through a spray gun, so that the sprayed shot flow can scatter and impact the surface of the metal part, and a corresponding residual stress field is generated on the metal part, thereby realizing the effects of strengthening the metal part and strengthening the fatigue resistance. The shot peening strengthening process has the advantages of simple operation process and obvious effect, and is widely applied to various fields of aerospace, motorcycles, nuclear power, automobiles and the like.
In the actual production process, the process file generally specifies the strengthening degree of shot peening required by different parts and different structures. The index for judging the degree of shot peening is the degree of bending of the Almen test piece. In order to accelerate the production, the important function of the process parameters for shot peening and the process window for the bending degree of the Almen test piece is established. It is clear that the greater the projectile energy, the greater the degree of bending of the almen test piece. The magnitude of the projectile's energy depends on the direction of the projectile's velocity, the magnitude, and the material of the projectile itself. Generally, on a workshop production site, most shot materials are fixed as a single material, the shot blasting angle is not changed basically based on the consideration of process operation convenience, and the shot blasting speed distribution becomes the most important constraint factor.
However, there are many factors that affect the shot peening effect of the shot peening process, such as the properties of the material itself, such as density, intensity, poisson's ratio, young's modulus, tangent modulus, etc., and the effects of the shot peening process parameters, such as shot type, shot size, shot shape, shot velocity, shot time, and shot coverage. Due to the complexity of the shot peening process and the multifactorial nature of the shot peening effect, various shot peening process parameters are often required to be selected and a large number of trial and error are often required to obtain the actual target shot peening effect, so that the cost for performing shot peening is high, and the time and economic cost are high.
In addition, the shot blasting machine is used for shot blasting test to determine shot blasting process parameters such as shot blasting pressure, flow, distance, angle and the like, the operation is complex, a large amount of manpower and material resources are consumed, and reasonable shot blasting parameters can be obtained by comparing a large amount of experimental data, so that the shot blasting process period is long, the cost is high, and the effect is slow. And once workshop equipment is upgraded and updated, the manual experience accumulated in the early stage cannot be quickly applied.
The existing numerical simulation method can perform corresponding simulation calculation on the shot peening process, so that a large amount of simulated stress strain is obtained, and the shot peening process is further researched. Moreover, the existing numerical simulation software can establish shot peening strengthening simulation models of single shots and array shots and can also establish random shot models to simulate the real shot peening strengthening process for simulation calculation, so that the authenticity of a simulation result is greatly improved. However, as the simulation model approaches the real shot peening simulation process, the larger the calculation scale of the simulation calculation is, the more the calculation amount is increased, so that the cost of performing the shot peening effect simulation calculation of the shot peening process is rapidly increased, and the configuration for performing the accurate and effective shot peening effect simulation calculation needs to be high and is expensive.
In addition, most of organizations or organizations for researching the peening process of the peening process are domestic and foreign colleges and research organizations, and the simulation result is corrected by lacking of industry practice knowledge and engineering experiment data, so that the simulation result often deviates from the actually achieved peening effect, the simulation result is not credible, the peening effect cannot be reliably predicted by the existing method, and the phenomenon that the peening process parameters cannot be recommended cannot be guided.
Therefore, the invention provides a novel shot peening intensity prediction method for shot peening process, which can effectively shorten simulation calculation time and reduce calculation cost, so that shot peening prediction result is consistent with engineering practice result. In addition, the method also extracts a shot peening mechanism and packages the shot peening mechanism into software, so that accurate prediction of shot peening and accurate recommendation of process parameters are facilitated.
Specifically, referring to fig. 1, the method for predicting the shot peening intensity for the shot peening process includes the steps of: establishing a millimeter-scale microscopic model of a standard Almen test piece in finite element software, and carrying out shot peening simulation calculation on the microscopic model based on preset shot peening process parameters to obtain residual stress on the microscopic model along the thickness direction;
establishing a macroscopic model of a standard Almen test piece, mapping the residual stress of the obtained microscopic model into the macroscopic model to be used as a boundary condition, and obtaining a residual stress model on the macroscopic model along the thickness direction through simulation calculation;
carrying out an actual shot peening test on the standard Almen test piece according to a preset shot peening process parameter so as to obtain actual residual stress data along the thickness direction on the standard Almen test piece;
and then, predicting the shot peening intensity of workpieces with different thicknesses under the preset shot peening process parameters by using the corrected residual stress model.
At present, finite element simulation software with strong functions such as Ansys, abaqus, NASTRAN/PATRAN, comsol-Multiphysics and the like can carry out numerical simulation calculation on the shot peening strengthening process. In this embodiment, the numerical simulation calculation of the shot peening process is performed by using Abaqus finite element software. A model of a standard Almen test piece is established in Abaqus finite element software to simulate the actual shot peening process, and shot peening residual stress data can be obtained through simulation calculation based on a finite element method. Firstly, the shape and the size of a millimeter-scale Almen test piece are obtained by carrying out equal-scale reduction according to the shape and the size of an actual standard Almen test piece, wherein the standard Almen test piece comprises a standard Almen test piece A, a standard Almen test piece C and a standard Almen test piece N. Subsequently, as shown in fig. 2, according to the shape and size of the millimeter-scale standard alp test piece, a corresponding micro model is established in finite element software, and the micro model corresponding to the standard alp test piece in shape and size is established to perform simulation calculation, so that the calculation amount of the simulation calculation is reduced, and the calculation cost and the configuration requirement of the simulation calculation are reduced.
After the micro model is established, the material is selected according to actual requirements or the material attribute parameters of the model, such as material density, material elastic modulus, poisson's ratio and the like, are input. In an actual shot blasting process, the spatial positions of the shots are random in the shot path, and the randomly distributed shots form a shot flow to impact the surface of a workpiece under high-speed motion. The random coordinates of the centers of the space shots are generated in finite element software, so that the shots are randomly distributed right above the model and do not interfere with each other, the pits formed when the shots impact on the model are not overlapped, and the generated shot bundles can effectively ensure that the surface of the model is impacted by the shots. And simultaneously setting shot peening process parameters such as material, size, quantity, speed direction, flow, shot peening pressure, distance, angle and the like of the shot. Preferably, a rigid body is selected to simulate the shot, and the strength and hardness of the rigid body are higher than those of a standard Almen test piece and a workpiece.
Finite element software is used for carrying out finite element simulation of a random multi-shot impact model, the outermost part is set to be an infinite element area, an infinite element is used as a reflection boundary, no stress wave is reflected by the boundary, and therefore the phenomenon that the result is incorrect due to the fact that the stress wave generated on the boundary is reflected and enters the model again is avoided. Local meshing can be performed according to the studied collision area and the shot blasting angle, so that the calculation efficiency is improved. Establishing a millimeter-scale microscopic model based on finite element software, and carrying out shot peening strengthening simulation calculation on the microscopic model by a finite element calculation method according to the Johnson-Cook model to obtain the residual stress distribution on the microscopic model shown in figure 3. And then extracting the residual stress along the thickness direction on the micro model by a statistical knowledge method of mean value and variance deviation value according to the residual stress distribution of the micro model, and obtaining a residual stress curve along the thickness direction on the micro model as shown in fig. 4. Wherein, the residual stress of the micro model is obtained by the following formula (1):
wherein δ is the material yield limit; a is the yield stress of the material; b is a material strain exponent coefficient; epsilon is the equivalent plastic strain of the material; n is a strain hardening index; c is a strain rate sensitivity coefficient; epsilon k Is a strain influence factor; t is k Is a temperature influencing factor; and m is a temperature sensitivity coefficient.
And establishing a macroscopic model of the standard Almen test piece in finite element software, selecting a structural mechanics physical field, mapping the obtained residual stress of the microscopic model as an input source to the macroscopic model as a boundary condition, and performing simulation calculation by a finite element method to obtain a residual stress model of the macroscopic model.
And (3) setting the same shot blasting parameters by using a shot blasting machine according to an industrial universal means to carry out shot blasting strengthening test on the standard Almen test piece so as to obtain the shot blasting strengthened standard Almen test piece. Then, acquiring an image of the surface of the standard Almen test piece subjected to shot peening test based on a machine vision technology, and identifying shot blasting on the surface of the standard Almen test piece to statistically calculate shot blasting coverage rate; and combining the peening parameters including the peening material, the peening size and the peening speed with the peening coverage rate to obtain actual residual stress data in the thickness direction on the standard Almen test piece. Or detecting the residual stress on the surface of the standard Almen test piece subjected to the shot peening test by using residual stress detection equipment or an electrolytic corrosion delamination method to obtain actual residual stress data along the thickness direction on the standard Almen test piece.
And then, correcting a relation curve between the shot peening process parameters and the shot peening strength according to general industry knowledge that the shot peening flow is reduced and the peening strength can be increased, and adjusting the influence factor parameters and the residual stress curve corresponding to the influence factor parameters during the simulation of the shot peening process until the residual stress in the simulation calculation of the shot peening process is basically consistent with the actual residual stress data, so that reliable shot peening simulation and a reliable residual stress model under the shot peening process parameters are obtained.
Thus, by means of the parameter correction method, a set of shot peening process parameters corresponds to a set of residual stress curves. And the step of predicting the shot peening strengthening by using the residual stress model further comprises the steps of obtaining residual stress data of the workpiece with the specific thickness along the thickness direction according to the residual stress model, calculating an arc height value corresponding to the residual stress data by using a universal arc height test method, and obtaining the shot peening strength of the corresponding shot peening strengthening process according to the calculated arc height value. The shot strength is obtained by calculating an arc height value using a residual stress distribution after shot peening according to an international common arc height test method for determining shot strength.
According to some preferred embodiments of the present invention, the method for predicting shot strength for a shot peening process further includes predicting shot strength for shot peening workpieces of different thicknesses under predetermined shot peening process parameters using a residual stress model of the macro model.
According to some preferred embodiments of the present invention, the method for predicting shot strength for a shot peening process further includes the steps of: changing the numerical value of one or more of the shot peening process parameters for multiple times to obtain corrected residual stress models under different shot peening process parameters; and obtaining a relation curve between the shot peening process parameters and the shot peening strength of the workpieces with the same thickness and establishing a corresponding parameter strength relation model. And recommending one or more values of the shot peening process parameters according to the parameter intensity relation model and the relation curve between the shot peening process parameters and the shot peening intensity based on the target shot peening intensity value of the user.
According to some preferred embodiments of the present invention, the method for predicting the peening intensity of the shot peening process further includes encapsulating the corrected residual stress model and the parameter intensity relationship model in application software, so as to facilitate prediction of the peening intensity under the predetermined shot peening process parameter in the shot peening process or recommendation of the shot intensity process parameter under the target peening intensity value.
Aiming at the shot peening process, the invention creates a reverse iteration optimization method combining simulation analysis and experimental data, so that the simulation analysis prediction result is matched with the engineering practice result, and the shot peening mechanism is extracted and can be packaged into software, thereby realizing the accurate prediction of shot peening and the accurate recommendation of process parameters.
While specific embodiments of the invention have been described above, it will be understood by those skilled in the art that these are by way of example only, and that the scope of the invention is defined by the appended claims. Various changes or modifications to these embodiments may be made by those skilled in the art without departing from the principle and spirit of this invention, and these changes and modifications are within the scope of this invention.
Claims (12)
1. A prediction method of shot peening intensity for shot peening process, the prediction method comprising:
establishing a millimeter-scale microscopic model of a standard Almen test piece, and carrying out shot peening simulation calculation on the microscopic model based on preset shot peening process parameters to obtain residual stress on the microscopic model along the thickness direction;
establishing a macroscopic model of a standard Almen test piece, mapping the residual stress of the obtained microscopic model into the macroscopic model to be used as a boundary condition, and obtaining a residual stress model on the macroscopic model along the thickness direction through simulation calculation;
performing an actual shot peening test on the standard Almen test piece according to the preset shot peening process parameters to obtain actual residual stress data along the thickness direction on the standard Almen test piece;
and correcting the residual stress model of the macroscopic model based on the actual residual stress data to obtain a corrected residual stress model, and then predicting the shot peening intensity of the workpieces with different thicknesses under the preset shot peening process parameters by using the corrected residual stress model.
2. The prediction method according to claim 1, further comprising:
and predicting the shot peening intensity of the workpieces with different thicknesses under the preset shot peening process parameters by using the residual stress model of the macroscopic model.
3. The prediction method of claim 1, wherein the shot peening process parameters include one or more of: shot material, shot size, shot velocity, shot jet direction, shot flow rate, shot pressure, shot distance, and shot angle.
4. The prediction method according to claim 3, further comprising:
changing the numerical value of one or more of the shot peening process parameters for multiple times to obtain corrected residual stress models under different shot peening process parameters;
and obtaining a relation curve between the shot peening process parameters and the shot peening strength of the workpieces with the same thickness and establishing a corresponding parameter strength relation model.
5. The prediction method according to claim 4, further comprising:
and recommending one or more values of the shot peening process parameters according to the parameter intensity relation model and the relation curve between the shot peening process parameters and the shot peening intensity based on the target shot peening intensity value of the user.
6. The prediction method according to claim 5, further comprising:
and encapsulating the corrected residual stress model and the parameter intensity relation model in application software.
7. The prediction method according to claim 1, wherein the step of obtaining the residual stress in the thickness direction on the microscopic model comprises:
establishing the millimeter-scale microscopic model based on finite element software, and carrying out shot peening strengthening simulation calculation on the microscopic model by a finite element calculation method to obtain the residual stress distribution on the microscopic model.
8. The prediction method of claim 7, wherein the step of obtaining the residual stress in the thickness direction on the micro model further comprises:
and extracting the residual stress along the thickness direction on the microscopic model by a statistical knowledge method according to the residual stress distribution of the microscopic model.
9. The prediction method of claim 1, wherein the step of obtaining actual residual stress data comprises:
acquiring an image of the surface of a standard Almen test piece subjected to shot peening strengthening test based on a machine vision technology, and identifying shot peening of the surface of the standard Almen test piece to calculate shot peening coverage rate in a statistical manner;
and combining the peening parameters including the peening material, the peening size and the peening speed with the peening coverage rate to obtain actual residual stress data in the thickness direction on the standard Almen test piece.
10. The prediction method of claim 1, wherein the step of obtaining actual residual stress data comprises:
and detecting the residual stress on the surface of the standard Almen test piece subjected to the shot peening test by using residual stress detection equipment or an electrolytic corrosion delamination method to obtain actual residual stress data on the standard Almen test piece in the thickness direction.
11. The prediction method according to any one of claims 1 to 10, wherein the step of predicting shot peening using a residual stress model comprises:
and obtaining residual stress data of the workpiece with the specific thickness along the thickness direction according to the residual stress model, calculating an arc height value corresponding to the residual stress data by using a general arc height test method, and obtaining the shot peening intensity of the corresponding shot peening strengthening process according to the calculated arc height value.
12. The method of predicting according to claim 1, wherein the standard Almen strips comprise standard Almen strip A, standard Almen strip C and standard Almen strip N.
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