Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Springback analysis in air bending process through experiment based artificial neural networks
Date
2014-10-24
Author
Senol, Ozgu
Esat, Volkan
Darendeliler, Haluk
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
207
views
0
downloads
Cite This
Sheet metal bending is one of the most frequently used sheet metal forming processes in manufacturing industry. This study investigates bending parameters and springback phenomenon of a stainless-steel sheet in air bending process. In most of the applications, springback is determined either by trial and error procedures or by using numerical methods. Artificial Neural Network (ANN) approach has proved to be a helpful tool for the engineers. ANN is used in this study to predict the springback amounts of stainless steel sheets through experiment based networks. Air bending process is first modeled and analyzed by a commercial finite element code. Springback amounts for different sheet thicknesses and bend angles are computed. In addition to computational modeling, experimentation of the air bending processes is carried out and experimental results are used in artificial neural network development to show the feasibility of ANN based on experimentation. Experimental outcome is also used for validation of the FE analysis of the process, which demonstrates good agreement. It is observed that ANN can be applied effectively to determine springback in air bending process, which embodies significant potential to determine air bending process parameters for industrial applications such as punch stroke.
Subject Keywords
Finite element method
,
Neural network
,
Air bending
URI
https://hdl.handle.net/11511/32911
DOI
https://doi.org/10.1016/j.proeng.2014.10.131
Collections
Department of Mechanical Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Finite element analysis of shearing processes
Isbir, S.; Darendeliler, Haluk; Gökler, Mustafa İlhan (null; 2006-12-01)
Shearing of sheet metal is extensively used in manufacturing industry. It is a highly non-linear process and involves complex mechanisms such as crack initiation and propagation, and deliberate fracture of the material for obtaining the desired geometry. Finite element method can be used for the design of shearing process. However, most of the general purpose commercial finite element analysis programs do not provide ready-to-use facilities to simulate shearing. In this paper, finite element simulation of s...
Springback analysis in bending through finite element method based artificial neural networks
Şenol, Özgü; Darendeliler, Haluk; Esat, Volkan; Department of Mechanical Engineering (2013)
Springback prediction is vital in order to obtain the desired part shape in metal forming processes. In most of the applications, springback amount is determined by trial and error procedures, and recently by using numerical methods or through handbook tables. Artificial Neural Network (ANN) is a helpful tool for the engineers and applied in this study to determine the springback amounts in air, V-die and wipe bending processes. For this purpose, bending processes are analyzed by commercial finite element (...
Finite element analysis of springback in bending of aluminium sheets
Esat, V; Darendeliler, Haluk; Gokler, MI (2002-04-01)
Bending is one of the processes frequently applied during manufacture of aluminium components. The bending operation involves springback, which is defined as elastic recovery of the part during unloading. In manufacturing industry, it is still a practical problem to predict the final geometry of the part after springback and to design appropriate tooling in order to compensate for springback. In this paper, commercially available finite-element analysis (FEA) software is used to analyse bending and springba...
Analysis of warm forging process
Aktakka, Gülgün; Darendeliler, Haluk; Department of Mechanical Engineering (2006)
Forging is a metal forming process commonly used in industry. Forging process is strongly affected by the process temperature. In hot forging process, a wide range of materials can be used and even complex geometries can be formed. However in cold forging, only low carbon steels as ferrous material with simple geometries can be forged and high capacity forging machinery is required. Warm forging compromise the advantages and disadvantages of hot and cold forging processes. In warm forging process, a product...
Finite Element Modelling of TBC Failure Mechanisms by Using XFEM and CZM
Bostanci, Safa Mesut; Gürses, Ercan; Çöker, Demirkan (Elsevier BV; 2019-01-01)
Thermal Barrier Coatings have been widely used in modern turbine engines to protect the nickel based metal substrate from the high temperature service conditions, 1600-1800 K. In this study, failure mechanisms of typical Air Plasma Sprayed Thermal Barrier Coatings (TBC) used in after-burner structures composed of three major layers: Inconel 718 substrate, NiCrAlY based metallic bond coat (BC) and Yttria Stabilized Zirconia (YSZ) based ceramic top coat (TC) are investigated. Investigation of the cracking mec...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
O. Senol, V. Esat, and H. Darendeliler, “Springback analysis in air bending process through experiment based artificial neural networks,” 2014, vol. 81, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32911.