Prediction of metal sheet forming based on a geometrical model approach

Froitzheim, Pascal; Stoltmann, Michael; Fuchs, Normen; Woernle, Christoph; Flügge, Wilko

doi:10.1007/s12289-019-01529-9

Cited by 12 publications

(9 citation statements)

References 12 publications

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“…Liu et al (2007) used the genetic algorithm to optimize a backpropagation neural network (BPNN) and built the springback prediction model of the U-bend of the plate. Froitzheim et al (2019) established a new artificial neural network model to predict the geometric parameters related to metal sheet forming. Serban et al (2020) developed an artificial neural network (ANN) model for springback prediction in the case of free cylindrical bending of metal sheets.…”

Section: Carried Outmentioning

confidence: 99%

Prediction of springback in local bending of hull plates using an optimized backpropagation neural network

Zhao

et al. 2021

Mech. Sci.

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Abstract. Springback is an inevitable problem in the local bending process of hull plates, which leads to low processing efficiency and affects the assembly accuracy. Therefore, the prediction of the springback effect, as a result of the local bending of hull plates, bears great significance. This paper proposes a springback prediction model based on a backpropagation neural network (BPNN), considering geometric and process parameters. Genetic algorithm (GA) and improved particle swarm optimization (PSO) algorithms are used to improve the global search capability of BPNN, which tends to fall into local optimal solutions, in order to find the global optimal solution. The result shows that the proposed springback prediction model, based on the BPNN optimized by genetic algorithm, is faster and offers smaller prediction error on the springback due to local bending.

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Section: Carried Outmentioning

confidence: 99%

Prediction of springback in local bending of hull plates using an optimized backpropagation neural network

Zhao

et al. 2021

Mech. Sci.

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“…In 1998, Forcellese et al [ 34 ] evaluated the effect of the training set size of ANN on the reliability of the prediction of the springback in the free-bending process, which was also presented in the overview work by Pattanaik in 2013 [ 35 ]. In the following years, the ANN methods were further developed and applied to several forming technologies including deep drawing [ 36 ], ring rolling [ 37 ], electrohydraulic forming [ 38 ], bending [ 39 , 40 ], incremental forming [ 41 ], and several other application areas [ 42 , 43 , 44 , 45 , 46 ]. Hamouche et al [ 47 ] have developed a novel approach to select and classify a sheet metal process by machine-learning method from the final part geometry and achieved an accuracy of 89%.…”

Section: Introductionmentioning

confidence: 99%

Finite Element Simplifications and Simulation Reliability in Single Point Incremental Forming

et al. 2022

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Single point incremental forming (SPIF) is one of the most promising technologies for the manufacturing of sheet metal prototypes and parts in small quantities. Similar to other forming processes, the design of the SPIF process is a demanding task. Nowadays, the design process is usually performed using numerical simulations and virtual models. The modelling of the SPIF process faces several challenges, including extremely long computational times caused by long tool paths and the complexity of the problem. Path determination is also a demanding task. This paper presents a finite element (FE) analysis of an incrementally formed truncated pyramid compared to experimental validation. Focus was placed on a possible simplification of the FE process modelling and its impact on the reliability of the results obtained, especially on the geometric accuracy of the part and bottom pillowing effect. The FE modelling of SPIF process was performed with the software ABAQUS, while the experiment was performed on a conventional milling machine. Low-carbon steel DC04 was used. The results confirm that by implementing mass scaling and/or time scaling, the required calculation time can be significantly reduced without substantially affecting the pillowing accuracy. An innovative artificial neural network (ANN) approach was selected to find the optimal values of mesh size and mass scaling in term of minimal bottom pillowing error. However, care should be taken when increasing the element size, as it has a significant impact on the pillow effect at the bottom of the formed part. In the range of selected mass scaling and element size, the smallest geometrical error regarding the experimental part was obtained by mass scaling of 19.01 and tool velocity of 16.49 m/s at the mesh size of 1 × 1 mm. The obtained results enable significant reduction of the computational time and can be applied in the future for other incrementally formed shapes as well.

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Section: Introductionmentioning

confidence: 99%

“…2,3 Due to these advantages, pressbrake bending has become a common method used in the manufacture of aircrafts (such as Ukraine An series large transport aircrafts, Russia Su-27, Su-303, fourthgeneration fighters and the aircrafts with high rib net panels 4,5 ), ship hull structures, marine engineering structures, and many other manufacturing areas. 6,7 Pressbrake bending is a very intricate multi-pass bending forming process, the forming quality and accuracy of the bent part will be influenced by loading path, the continuous change of the deformation area, the bending load and other process parameters, etc. Many of the above factors lead to problems such as springback, rupture, and cross-sectional deformation of the 1 workpiece during the bending process.…”

Section: Introductionmentioning

confidence: 99%

Residual stress analysis and its effect on springback for multi-step pressbrake bending

Zhang

Tian

Wang

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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Pressbrake bending is a multi-step metal forming process and widely applied in the fields of aerospace, shipbuilding, and offshore industry. However, the bending residual stress that arise from the previous-pass bending step will affect the stress distribution of the next-pass bending step, and finally affect the springback after unloading, which results in the difficulty to guarantee the dimensional accuracy of the forming part. In this study, the residual stresses for multi-pass pressbrake forming and its influence on springback are systematically investigated. First, the analytical model for the residual stress of the bent specimen with initial curvature is deduced based on geometric relationship and elastic–plastic bending theory. Second, the calculation method of rigid body position redefine to simulate the multi-pass bending forming process is developed, and the numerical method is confirmed by comparison with experimental method. The analytical model to predict the residual stress is shown to be in close agreement with results from numerical method, demonstrating the validity of the analytical model. Finally, the impact of residual stress in single-pass and multi-pass bending forming on springback are discussed thoroughly. It is found that, as the number of forming passes increases, the equivalent plastic strain will increase to 9% of three-pass loading from 7.8% of one-pass loading, which explains the reason of increasing the number of bending passes can not only reduce springback, but also reduce the residual effect of bending forming.

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Prediction of metal sheet forming based on a geometrical model approach

Cited by 12 publications

References 12 publications

Prediction of springback in local bending of hull plates using an optimized backpropagation neural network

Prediction of springback in local bending of hull plates using an optimized backpropagation neural network

Finite Element Simplifications and Simulation Reliability in Single Point Incremental Forming

Residual stress analysis and its effect on springback for multi-step pressbrake bending

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