Artificial Neural Network Analysis of Springback in V Bending

Bozdemir, Mustafa; Gölcü, Mustafa

doi:10.3923/jas.2008.3038.3043

Cited by 24 publications

(10 citation statements)

References 16 publications

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“…The literature review shows that FEM methods and Machine Learning approaches are the two techniques that are vastly applied to predict the springback in sheet metals. Since FEM is slow so it cannot be used as an on-line tool in the production line for predicting springback [29]. In machine learning, most of the earlier attempts used artificial neural networks (ANN) to predict springback, which has several limitations.…”

Section: Fig 3 Springback In Sheet-metals Bendingmentioning

confidence: 99%

Machine Learning for the Prediction of Springback in High Tensile Strength Steels after V-Bending Process Using Tree-Based Learning

Baig

Wasif

Fatima

et al. 2021

Preprint

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Sheet metal bending is a typical operation and springback is an unintended consequence of this operation. Since it causes fitting issues in the assembly, which leads to quality problems, anticipating it long before the bending operation is done is essential in today's production, so that machining parameters can be adjusted accordingly. In order to predict springback with minimum errors, this paper presents the idea for the development of machine learning models using tree-based learning algorithms (A class of machine learning algorithms). Tree-based learning algorithms are employed because they are precise, consistent, and easy to understand. Experimental studies provided the data for training and testing the models. The model's input parameters were sheet Material, Thickness, Width, Initial Angle (Desired angle), and Machine used to perform the bending. Following the training and testing of different tree-based learning algorithms, the results were evaluated using MAE and MSE. It was determined that Gradient boosting algorithms (a class of tree-based learning) gave the best results. Later on further evaluation of algorithms, it was found that LightGBM produced the best results, with MAE and MSE of 0.41 and 0.25, respectively.

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Section: Fig 3 Springback In Sheet-metals Bendingmentioning

confidence: 99%

Machine Learning for the Prediction of Springback in High Tensile Strength Steels after V-Bending Process Using Tree-Based Learning

Baig

Wasif

Fatima

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…A neural network is a set of fundamental processing elements or neurons and connections with adjustable weights, attempting to simulate the human brain as well as the biological neural networks function. ANNs have been successfully used to solve problems in various sectors such as engineering, neurology, medicine, mathematics, and others [19]. In case of a multi-layer NN, the network consists of an input layer, one or more hidden layers and an output layer.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Springback Prediction in Sheet Metal Forming, Based on Finite Element Analysis and Artificial Neural Network Approach

Spathopoulos

Stavroulakis

2020

Applied Mechanics

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Sheet metal forming is one of the most important manufacturing processes applied in many industrial sectors, with the most prevalent being the automotive and aerospace industries. The main purpose of that operation is to produce a desired formed shape blank, without any material failures, which should lie well within the acceptable tolerance limits. Springback is affected by factors such as material properties, sheet thickness, forming tools geometry, contact and friction, etc. The present paper proposes a novel neural network system for the prediction of springback in sheet metal forming processes. It is based on Bayesian regularized backpropagation networks, which have not been tested in the literature, according to the authors’ best knowledge. For the creation of training examples a carefully prepared Finite Element model has been created and validated for a test case used in similar industrial studies.

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“…Further they demonstrated how the signature of the force-displacement relation changes significantly with increasing tool wear in a typical configuration of sheet steel blanking. Bozdemir and Golcu [43] used ANN to determine the effects of material, bending angle and ratio of bend angle to sheet thickness on spring-back angle. Training of the network is performed using Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Polo-Ribiere Conjugate Gradient (CGP) back propagation algorithms.…”

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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Artificial Neural Network Analysis of Springback in V Bending

Cited by 24 publications

References 16 publications

Machine Learning for the Prediction of Springback in High Tensile Strength Steels after V-Bending Process Using Tree-Based Learning

Machine Learning for the Prediction of Springback in High Tensile Strength Steels after V-Bending Process Using Tree-Based Learning

Springback Prediction in Sheet Metal Forming, Based on Finite Element Analysis and Artificial Neural Network Approach

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

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