Prediction of springback in sheet metal components with holes on the bending area, using experiments, finite element and neural networks

Nasrollahi, Vahid; Arezoo, Behrooz

doi:10.1016/j.matdes.2011.11.039

Cited by 52 publications

(24 citation statements)

References 17 publications

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“…Springback can be defined as the elastic distortion that occurs as a result of the forming process so that the shape produced is not the desired shape. The springback effect is related to both manufacturing parameters and material properties [5,15,12]. There has been substantial reported work on springback characterization, analysis and prediction.…”

Section: Overview Of Related Workmentioning

confidence: 99%

“…Although FEM provides a flexible simulation environment (parameters can be easily modified) FEM is an expensive and time-consuming option [17,5]. Furthermore, FEM is not an accurate prediction method due to the simplification assumptions that must be made [2,3,15]. Artificial Neural Network (ANNs) are often quoted as being a good alternative to FEM.…”

Section: Overview Of Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Identification of Correlations Between 3D Surfaces Using Data Mining Techniques: Predicting Springback in Sheet Metal Forming

El-Salhi

Coenen

Dixon

et al. 2012

Research and Development in Intelligent Systems XXIX

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A classification framework for identifying correlations between 3D surfaces in the context of sheet metal forming, especially Asymmetric Incremental Sheet Forming (AISF), is described. The objective is to predict "springback", the deformation that results as a consequence of the application of a sheet metal forming processes. Central to the framework there are two proposed mechanisms to represent the geometry of 3D surfaces that are compatible with the concept of classification. The first is founded on the concept of a Local Geometry Matrix (LGM) that concisely describes the geometry surrounding a location in a 3D surface. The second, is founded on the concept of a Local Distance Measure (LDM) derived from the observation that springback is greater at locations that are away from edges and corners. The representations have been built into a classification framework directed at the prediction of springback values. The proposed framework and representations have been evaluated using two surfaces, a small and a large flat-topped pyramid, and by considering a variety of classification mechanisms and parameter settings.

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Section: Overview Of Related Workmentioning

confidence: 99%

Section: Overview Of Related Workmentioning

confidence: 99%

Identification of Correlations Between 3D Surfaces Using Data Mining Techniques: Predicting Springback in Sheet Metal Forming

El-Salhi

Coenen

Dixon

et al. 2012

Research and Development in Intelligent Systems XXIX

View full text Add to dashboard Cite

show abstract

“…The springback can be represented by angular displacement, and the arc radius and angle after springback can be calculated rapidly by using an analytical method, such as for the U-section part [10,18] or arcs of an arbitrary channel [2], or knowledge-based method, such as neural network [9,19], or three-dimensional scanning data comparison method [28]. As some practical process conditions cannot be taken into account, there are usually some deviations between the predicted value and practical one by analytical calculation [2].…”

Section: Introductionmentioning

confidence: 99%

A rapid and intelligent approach to design forming shape model for precise manufacturing of flanged part

Liu

Yang

et al. 2017

Int J Adv Manuf Technol

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Adjusting the part shape with complex flanges to compensate springback deformation is key to forming shape design for manufacturing rapidly and precisely. Classical forming shape design by displacement adjustment (DA) method using finite element (FE) simulation is usually time-consuming and not accurate enough for complex surface part in industrial application. In this paper, the forming shape is modeled by changing the relations of geometric features of part model with the new flange control surfaces directly. Control surface processing (CSP) method is presented including control surface trimming, cross section division, springback compensation, and extending to design forming shape model of doubly curved flange part with joggles rapidly. The algorithms of cross section curves division of control surfaces and subsequent subdivision of each curve with circular arc and line segments are proposed. A case-based reasoning (CBR) technique and gray relation analysis (GRA) are used to support the intelligent springback prediction of each bending segment of the cross section curve. The geometric data of control surface is expressed in XML format to realize the integration of the CAD-based tools of control surface division and compensation with the Web-based springback prediction system. The approach is demonstrated on an industrial aircraft wing rib part. The forming shape model could be designed rapidly by comparison with DA method. The part shape deviations of flange angle (−0.465°~0.528°) and surface position (−0.3 mm~0.3 mm) were detected by comparing the desired geometry with the actual digital formed part shape, and the results indicate that the approach can achieve the industrial part manufacturing rapidly and precisely.

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“…Some researchers had study the effect of the process parameter to springback behaviour. Nasrollahi & Arezoo [4] found that springback phenomenon can be separated into two categories. First are factors which are more related to material issue such as Young's modulus, yield stress, strength coefficient, strain hardening, Poisson's ratio and anisotropic coefficient.…”

Section: Introductionmentioning

confidence: 99%

Effect of annealing, thickness ratio and bend angle on springback of AA6061-T6 with non-uniform thickness section

2016

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Abstract. Non-uniform thickness section section is considered one of the most effective approaches to reduce automotive part weight. Reduction in term of mass and size result in less fuel consumption and greenhouse gases. Thickness is the most significant parameter to formability, therefore forming a section with non-uniform thickness becomes a great challenge. Improper process and incorrect decision may lead to severe defect and one of the main concerns is the springback. This study will focus on springback behaviour of non-uniform thickness AA6061 strip with complex profile using Taguchi Method. Profile projector (PC 3000) is used to measure the spring back and two-line technique is applied to measure angles (after loading) between two lines. Three parameters (i.e. annealing temperature, thickness ratio and bend angle) are studied, and results determine that the most significant parameter is bend angle, followed by thickness ratio, and then by the annealing temperature of the specimen during bending process.

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Prediction of springback in sheet metal components with holes on the bending area, using experiments, finite element and neural networks

Cited by 52 publications

References 17 publications

Identification of Correlations Between 3D Surfaces Using Data Mining Techniques: Predicting Springback in Sheet Metal Forming

Identification of Correlations Between 3D Surfaces Using Data Mining Techniques: Predicting Springback in Sheet Metal Forming

A rapid and intelligent approach to design forming shape model for precise manufacturing of flanged part

Effect of annealing, thickness ratio and bend angle on springback of AA6061-T6 with non-uniform thickness section

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