Application of Neural Network in Sheet Metal Bending Process

Pathak, K. K.; Panthi, S.K.; Ramakrishnan, N.

doi:10.14429/dsj.55.1976

Cited by 22 publications

(10 citation statements)

References 3 publications

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“…Back propagation algorithm is used for training purpose. Pathak et al [36] studied prediction responses of the sheet metal bending process using ANN. Forty four cases are analyzed using FEM to train NN.…”

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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“…Artificial neural networks, among the various types of learning algorithms, are widely used in sheet metal forming processes due to their ability to overcome the limitations imposed by nonlinearities and the multiple parameters involved in forming problems. Several articles on air bending have been published, following the use of artificial neural networks [23][24][25][26][27]. The authors of [28] studied the use of ANN on modeling the air Vbending processes using both an analytical and experimental data set and demonstrated the capability of ANN to model the springback problem.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning to Bending Processes and Material Identification

Cruz

Barbosa

Santos

et al. 2021

Metals

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The increasing availability of data, which becomes a continually increasing trend in multiple fields of application, has given machine learning approaches a renewed interest in recent years. Accordingly, manufacturing processes and sheet metal forming follow such directions, having in mind the efficiency and control of the many parameters involved, in processing and material characterization. In this article, two applications are considered to explore the capability of machine learning modeling through shallow artificial neural networks (ANN). One consists of developing an ANN to identify the constitutive model parameters of a material using the force–displacement curves obtained with a standard bending test. The second one concentrates on the springback problem in sheet metal press-brake air bending, with the objective of predicting the punch displacement required to attain a desired bending angle, including additional information of the springback angle. The required data for designing the ANN solutions are collected from numerical simulation using finite element methodology (FEM), which in turn was validated by experiments.

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“…20 Several articles on air bending have been published, following the use of FEA and NN, although being mostly applied to other sheet metal bending processes. 21,22 On the contrary, Inamdar et al 23 explored the use of NN on modeling springback in air V-bending by using a set of analytical and experimental data. The NN estimated the springback angle and the punch displacement having as input a given tool geometry data, the bending angle and the sheet material properties.…”

Section: Introductionmentioning

confidence: 99%

“…Several articles on air bending have been published, following the use of FEA and NN, although being mostly applied to other sheet metal bending processes. 21,22…”

Section: Introductionmentioning

confidence: 99%

Forming and springback prediction in press brake air bending combining finite element analysis and neural networks

Miranda

Barbosa

Santos

et al. 2018

The Journal of Strain Analysis for Engineering Design

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Press brake air bending, a process of obtaining products by sheet metal forming, can be considered at first sight a simple geometric problem. However the accuracy of the obtained geometries involves the combination of multiple parameters directly associated with the tools and the processing parameters, as well as with the sheet metal materials and dimensions. The main topic herein presented deals with the capability of predicting the punch displacement process parameter that enables the product to be accurately shaped to a desired bending angle, in press brake air bending. In our approach, it is considered separately the forming process and the elastic recovery (i.e. the springback effect). Current solutions in press brake numerical control (computer numerical control) are normally configured by analytical models developed from geometrical analysis and including correcting factors. In our approach, it is proposed to combine the use of a learning tool, artificial neural networks, with a simulation and data generation tool (finite element analysis). This combination enables modeling the complex nonlinear behavior of the forming process and springback effect, including the validation of results obtained. A developed model taking into account different process parameters and tool geometries allow extending the range of applications with practical interest in industry. The final solution is compatible with its incorporation in a computer numerical control press brake controller. It was concluded that, using this methodology, it is possible to predict efficient and accurate final geometries after bending, being also a step forward to a ''first time right'' solution. In addition, the developed models, methodologies and obtained results were validated by comparison with experimental tests.

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Application of Neural Network in Sheet Metal Bending Process

Cited by 22 publications

References 3 publications

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

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

Application of Machine Learning to Bending Processes and Material Identification

Forming and springback prediction in press brake air bending combining finite element analysis and neural networks

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