Using genetic algorithm-back propagation neural network prediction and finite-element model simulation to optimize the process of multiple-step incremental air-bending forming of sheet metal

Fu, Zemin; Mo, Jinhan; Lin, Cheng-Xian; Chen, Wei

doi:10.1016/j.matdes.2009.06.019

Cited by 86 publications

(24 citation statements)

References 11 publications

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“…One hidden layer is used and the number of neurons of the hidden layers is five. Fu et al [52] developed three-layer back propagation NN to predict punch radius based on the results of air-bending experiments of sheet metal. GA is used to optimize the weights of NN for minimizing the error between the predictive punch radius and the experimental one.…”

Section: Summingmentioning

confidence: 99%

Applications of Artificial Neural Network to Sheet Metal Work - A Review

Kashid¹,

Kumar²

2013

AJIS

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Design of sheet metal components and press tools demands a high level of knowledge and industrial experience on the part of designers. Recently, various artificial intelligence (AI) techniques are being used in sheet metal work to reduce complexity; minimize the dependency on human expertise and time taken in design of parts and dies as well as to improve the design efficiency. Artificial neural network (ANN) technique is one of the most powerful tools for solving engineering design problems and minimizing errors in experimental data. This paper describes a comprehensive review of applications of ANN technique to sheet metal work. Major published research work in the domain area is summarized in tabular form. Based on the critical review of available literature, further research scope is identified. The present literature review reveals that there is stern need to apply ANN technique to press tool design and to predict tool life in sheet metal industries.

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Section: Summingmentioning

confidence: 99%

Applications of Artificial Neural Network to Sheet Metal Work - A Review

Kashid¹,

Kumar²

2013

AJIS

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“…An alternative approach described in [13] used a genetic algorithm in order to optimize the weights for the nodes of the ANN for the purpose of springback prediction. Nevertheless, computational resources requirements remain the main limitation of ANNs [13,6]. To the best knowledge of the authors there is no reported work on the application of classification techniques (or data mining techniques in general) for the purpose of predicting springback in the context of AISF.…”

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

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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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“…The method was applied on a U shape part. The artificial neural networks were used in [21] to predict the punch radius based on the results of air-bending experiments of sheet metals. A genetic algorithm was used to optimize the weights of neural network and then, with the predicted punch radius and other geometrical parameters of a tool, 2D and 3D ABAQUS finite-element models were established.…”

Section: Introductionmentioning

confidence: 99%

Research Concerning the Springback Prediction in the Bending Operations

Şerban

Bâlc

Achimaş

et al. 2013

AEF

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Abstract. Nowadays firms are required to obtain high quality products in order to increase their competitiveness. The time required to obtain a new product is also essential to fight the concurrence. For manufacturers of bent parts, accurate prediction of the springback is very important. Therefore, this paper investigates the applicability of artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) to predict the springback in the free cylindrical bending process of metallic sheets. The finite element method (FEM) was used to simulate the springback in the free cylindrical bending process and the results were used as training data for ANN and ANFIS. The finite element results were validated by comparison with experimental data. Statistic criteria were used to evaluate the performance of the developed ANN and ANFIS models. It was found that the predictions are in good agreement with the FEM data.

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Using genetic algorithm-back propagation neural network prediction and finite-element model simulation to optimize the process of multiple-step incremental air-bending forming of sheet metal

Cited by 86 publications

References 11 publications

Applications of Artificial Neural Network to Sheet Metal Work - A Review

Applications of Artificial Neural Network to Sheet Metal Work - A Review

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

Research Concerning the Springback Prediction in the Bending Operations

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