Tube and Sheet Metal Forming Processes and Applications

Centeno, Gabriel; Silva, M.B.

doi:10.3390/met12040553

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…The challenges of high accuracy and mass production parts with good mechanical properties and fulfilling environmental requirements need to adopt a new trajectory in the recent industry. These requirements of the above challenges need to concentrate the concerns on the development of the metal forming sector through adopting an accurate procedure [5]. In work [6], the finite element method as computational modeling is used in each forming stage of analysis of the progressive deformation to make the design efficient, and faster, and decrease the trial and error time.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Implementation of finite element analysis for solving the constraints in forming process of large steel parts

Jawad

Jaber

Ibrihim

2022

EEJET

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In recent years, the demand for high-durability parts are rising too much. These challenges are difficult to overcome without an innovative framework based on an accurate database. The problems of high stress generated due to the hard friction and severe crystal dislocation during the forming process need to be solved. High frictional forces between the contact surfaces while forming lead to high sticking between the parts. In this work, forming process of the large sheet metal has been explored based on some parameters like material properties, stress generation, and their effects on the product quality. For this purpose, square sheet metal of 721*721*5 mm is considered, and the product formation through many forming steps was carried out. This work includes adopting some design steps, modeling, and analysis to control some parameters and minimize the generation stresses. The finite element software (ABAQUS/CAE) has been adopted for analyzing this process. In this simulation, the forming process evolution in different steps has been analyzed, and the influence of the effective parameters was performed. As a result, it’s found that the generation stresses are highly concentrated near the fillet zones and proportional to the pressure, and depend on the nature of contact and friction. Simulation results also revealed that the uniform pressure during forming will leads to minimizing the friction and stress generation (5 %) and this will improve the product quality. Also, it’s possible to identify and facilitate many difficulties and evaluate the possibilities before further investing in tooling. It’s concluded that any accurate process like this must depend on some sequence steps like design, modeling, and simulation. Moreover, folding the large surface area needs accurate and adjustable types of equipment.

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Implementation of finite element analysis for solving the constraints in forming process of large steel parts

Jawad

Jaber

Ibrihim

2022

EEJET

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“…As an important metal-forming process, sheet metal stamping is widely applied in the modern industry [ 1 , 2 ]. Springback is an inevitable physical phenomenon during the metal sheet-forming process [ 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of Lankford Coefficients on Springback Behavior during Deep Drawing of Stainless Steel Cylinders

Hong

Zhang

et al. 2023

Materials

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Accurate prediction of springback is increasingly required during deep-drawing formation of anisotropic stainless steel sheets. The anisotropy of sheet thickness direction is very important for predicting the springback and final shape of a workpiece. The effect of Lankford coefficients (r00, r45, r90) with different angles on springback was investigated using numerical simulation and experiments. The results show that the Lankford coefficients with different angles each have a different influence on springback. The diameter of the straight wall of the cylinder along the 45-degree direction decreased after springback, and showed a concave valley shape. The Lankford coefficient r90 had the greatest effect on the bottom ground springback, followed by r45 and then r00. A correlation was established between the springback of workpiece and Lankford coefficients. The experimental springback values were obtained by using a coordinate-measuring machine and showed good agreement with the numerical simulation results.

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“…There are methods of unconventional forming involving shaping with a rotating tool [1][2][3]. During deep drawing, a flat blank is transformed into a three-dimensional drawpiece with a non-expandable surface [4,5]. When forming non-axisymmetric parts, especially in the automotive industry, the dies usually require drawbeads.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks to the Analysis of Friction Behaviour in a Drawbead Profile in Sheet Metal Forming

Trzepieciński

Najm

2022

Materials

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Drawbeads are used when forming drawpieces with complex shapes to equalise the flow resistance of a material around the perimeter of the drawpiece or to change the state of stress in certain regions of the drawpiece. This article presents a special drawbead simulator for determining the value of the coefficient of friction on the drawbead. The aim of this paper is the application of artificial neural networks (ANNs) to understand the effect of the most important parameters of the friction process (sample orientation in relation to the rolling direction of the steel sheets, surface roughness of the counter-samples and lubrication conditions) on the coefficient of friction. The intention was to build a database for training ANNs. The friction coefficient was determined for low-carbon steel sheets with various drawability indices: drawing quality DQ, deep-drawing quality DDQ and extra deep-drawing quality EDDQ. Equivalents of the sheets tested in EN standards are DC01 (DQ), DC03 (DDQ) and DC04 (EDDQ). The tests were carried out under the conditions of dry friction and the sheet surface was lubricated with machine oil LAN46 and hydraulic oil LHL32, commonly used in sheet metal forming. Moreover, various specimen orientations (0° and 90°) in relation to the rolling direction of the steel sheets were investigated. Moreover, a wide range of surface roughness values of the counter-samples (Ra = 0.32 μm, 0.63 μm, 1.25 μm and 2.5 μm) were also considered. In general, the value of the coefficient of friction increased with increasing surface roughness of the counter-samples. In the case of LAN46 machine oil, the effectiveness of lubrication decreased with increasing mean roughness of the counter-samples Ra = 0.32–1.25 μm. With increasing drawing quality of the sheet metal, the effectiveness of lubrication increased, but only in the range of surface roughness of the counter-samples in which Ra = 0.32–1.25 μm. This study investigated different transfer functions and training algorithms to develop the best artificial neural network structure. Backpropagation in an MLP structure was used to build the structure. In addition, the COF was calculated using a parameter-based analytical equation. Garson partitioning weight was used to calculate the relative importance (RI) effect on coefficient of friction. The Bayesian regularization backpropagation (BRB)—Trainbr training algorithm, together with the radial basis normalized—Radbasn transfer function, scored best in predicting the coefficient of friction with R2 values between 0.9318 and 0.9180 for the training and testing datasets, respectively.

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Tube and Sheet Metal Forming Processes and Applications

Abstract: In the late 1960s, pioneer works by Keeler [...]

Cited by 5 publications

References 14 publications

Implementation of finite element analysis for solving the constraints in forming process of large steel parts

Implementation of finite element analysis for solving the constraints in forming process of large steel parts

Effect of Lankford Coefficients on Springback Behavior during Deep Drawing of Stainless Steel Cylinders

Application of Artificial Neural Networks to the Analysis of Friction Behaviour in a Drawbead Profile in Sheet Metal Forming

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