Optimal design of preform shape based on EFA-FEM-GA integrated methodology

Liu, Chengshang; Xu, Wujiao; Wang, Yu; Liu, Minyao

doi:10.1007/s12289-021-01620-0

Cited by 7 publications

(5 citation statements)

References 65 publications

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“…This method encodes potential solutions to the optimization problem into various chromosomes and, based on individual fitness values and three genetic manipulations of chromosome selection, crossover, and mutation, enables individuals to progressively evolve towards the optimal solution. The algorithm exhibits strong adaptability, robustness, and global search capabilities [18,28].…”

Section: Profile Of Svr and Gamentioning

confidence: 99%

“…The amalgamation of the finite element method and preformed part optimization de-sign has progressively garnered widespread application in analyzing and controlling forging process defects, employing techniques such as equipotential field [12,13], topology optimization [14][15][16], reverse tracking [10,17], elliptic Fourier analysis [18], and unequal thickness billet design [2,19]. By employing this method, defects can be meticulously traced to their exact location, and subsequent modifications or optimizations can be made to the billet as necessary.…”

Section: Introductionmentioning

confidence: 99%

“…The preform design problem is transmuted into an optimization problem, synthesizing optimization theory and forward simulation technology to actualize preform design and curtail its arbitrary nature. Numerous scholars have employed response surface methodology (RSM) [2,8,20], artificial neural networks (ANN) [21], convolutional neural networks (CNN) [22], genetic algorithms (GA) [8,18,23], and other intelligent algorithms to augment the precision and efficiency of pre-forging optimization methods.…”

Section: Introductionmentioning

confidence: 99%

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Research on deformation uniformity control of thin-walled conical aeroengine forgings based on GA-SVR

Han,

Wang,

et al. 2024

Int J Adv Manuf Technol

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The performance and service life of aero-engine forgings, vital components of aero-engines, are profoundly impacted by their forming quality, which is substantially influenced by deformation uniformity. This study aims to control the deformation uniformity of thin-walled conical aero-engine forgings through preform design. Initially, the DEFORM DOE module and MATLAB are utilized to simulate various scenarios, automatically acquiring samples with preform die geometry parameters as variables. Subsequently, a deformation uniformity prediction model is constructed by integrating genetic algorithm (GA) and support vector regression (SVR). The fitness function of the genetic algorithm is defined as the mean squared error (MSE) between the predicted values of the SVR model and the finite element simulation values, iteratively optimizing the SVR model. The results reveal that the established prediction model, based on limited sample data, achieves high accuracy and efficiency, significantly diminishing the frequency of trial-and-error simulations while enhancing the efficiency of preform design for thin-walled conical forgings. Finite element simulation outcomes and macro/microstructure test results following trial production further validate the deformation uniformity prediction model's exceptional accuracy.

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Section: Profile Of Svr and Gamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on deformation uniformity control of thin-walled conical aeroengine forgings based on GA-SVR

Han,

Wang,

et al. 2024

Int J Adv Manuf Technol

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“…In addition, it is possible to use other physical fields to describe the material flow and thus obtain the shape of the preform, for example, by integrating QForm metal forming simulation software with special CAD software that enables isothermal surface extraction to preform modeling [27]. With the development of random computer technology, a preform shape design method based on optimal design is widely used, which is to optimize the design variables related to the preform shape size to get the optimal solution with a particular index or a specific relative relationship as the objective function, such as response surface method (RSM) [29][30][31], sensitivity analysis [32,33], genetic algorithms (GA) [34][35][36], topological optimization [37,38] and reduced basis technique (RBT) [39].…”

Section: Introductionmentioning

confidence: 99%

“…The preform shape, no matter what design method is used to obtain it, needs to be verified by the forward forming process [27,31,36,38]. The difficulty of forwarding process analysis is parameter setting and constraint control, which reduces the number of trials and errors and improves optimization efficiency.…”

Section: Introductionmentioning

confidence: 99%

Automatic preform design and optimization for aeroengine disk forgings

Han

Wang

Chen

et al. 2023

Int J Adv Manuf Technol

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To ensure a more uniform microstructure distribution of forging and improve the service performance of aeroengine disk parts, an automated preform design method is proposed for integrated preform shape design and optimization based on the NURBS curve, finite element method (FEM), and genetic algorithm (GA). Firstly, the random preform shape graph is automatically constructed by the NURBS curve design criterion. The volume and shape complexity are used as the constraints of the preform. Then the ratio of the mesh area within the set strain range to the total mesh area is used as the fitness function for the uniformity of deformation, and the genetic algorithm module is used for optimization. Finally, a large disk forging is an example of its optimal design. The results show that the deformation uniformity of the forgings is excellent, its fitness value is as high as 99.59%, and there are no problems such as folding, underfilling, and limited distribution of flash, which verifies the effectiveness of the method. In addition, the method has the advantage of strong universality, which can find the preform shape with good deformation uniformity for any shape forgings.

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Automated design of multi-stage forging sequences for die forging

Hedicke-Claus

Kriwall

Stonis

et al. 2023

Prod. Eng. Res. Devel.

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Forgings are produced in several process steps, the so-called forging sequence. The design of efficient forging sequences is a very complex and iterative development process. In order to automate this process and to reduce the development time, a method is presented here, which automatically generates multi-stage forging sequences for different forging geometries on the basis of the component geometry (STL file). The method was developed for closed die forging. The individual modules of this forging sequence design method (FSD method) as well as the functioning of the algorithm for the generation of the intermediate forms are presented. The method is applied to different forgings with different geometrical characteristics. The generated forging sequences are checked with FE simulations for the quality criteria form filling and freedom from folds. The simulation results show that the developed FSD method provides good approximate solutions for an initial design of forging sequences for closed die forging in a short time.

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Optimal design of preform shape based on EFA-FEM-GA integrated methodology

Cited by 7 publications

References 65 publications

Research on deformation uniformity control of thin-walled conical aeroengine forgings based on GA-SVR

Research on deformation uniformity control of thin-walled conical aeroengine forgings based on GA-SVR

Automatic preform design and optimization for aeroengine disk forgings

Automated design of multi-stage forging sequences for die forging

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