The Study of Parameters on CK67 Steel Sheet Spring-Back in L-Dies Bending Processes

2011 ◽  
Vol 189-193 ◽  
pp. 2788-2791
Author(s):  
J. Bazrafshan ◽  
A. Gorji ◽  
A. Taghizadeh Armaky ◽  
J. Sadeghi
Keyword(s):  
Numerical Simulations ◽  
Sheet Metal ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Steel Sheet ◽  
Elastic Recovery ◽  
Sheet Thickness ◽  
Bend Angle ◽  
Spring Back ◽  
Geometric Changes

One of the most sensitive features of sheet metal forming processes is the elastic recovery during unloading, called spring-back, which leads to some geometric changes in the product. This phenomenon will affect bend angle and bend curvature, and can be influenced by various factors. In this research, the effects of sheet thickness and die radiuses on spring-back in L-die bending of CK67 and St 14 steel sheet were studied by experiments and numerical simulations.

The Effect of Anisotropy on Spring-Back of CK67 Steel Sheet in L-Dies Bending Processes

2011 ◽  
Vol 291-294 ◽  
pp. 672-675
Author(s):  
Jafar Bazrafshan ◽  
A. Gorji ◽  
A. Taghizadeh Armaky
Keyword(s):  
Numerical Simulations ◽  
Sheet Metal ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Steel Sheet ◽  
Elastic Recovery ◽  
Sheet Thickness ◽  
Bend Angle ◽  
Spring Back ◽  
Geometric Changes

One of the most sensitive features of sheet metal forming processes is the elastic recovery during unloading, called spring-back, which leads to some geometric changes in the product. This phenomenon will affect bend angle and bend curvature, and can be influenced by various factors. In this research, the effects of sheet thickness and die radiuses an sheet anisotropy on spring-back in L-die bending of CK67 steel sheet were studied by experiments and numerical simulations.

An Analytical Model to Reduce Spring-Back in Incremental Sheet Metal Forming (ISMF) Process

2009 ◽  
Vol 83-86 ◽  
pp. 1113-1120 ◽  
Author(s):  
Mehdi Vahdati ◽  
Mohammad Sedighi ◽  
Hossein Khoshkish
Keyword(s):  
Sheet Metal ◽  
Process Parameters ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Sheet Thickness ◽  
Spring Back ◽  
Step Size ◽  
Influence Of Process Parameters ◽  
Incremental Sheet Metal Forming ◽  
Tool Diameter

In this paper, spring-back and its effect on geometrical and dimensional accuracy of incremental sheet metal forming (ISMF) process has been studied. The influence of process parameters such as: vertical step size, sheet thickness, tool diameter, feed rate and spindle speed have been investigated. A series of experimental tests have been carried out for a straight groove bead-shape part made of aluminum sheets. A reliable statistical analysis has been carried out to extract the importance of each parameter. The obtained model permits to select appropriate process parameters to reduce spring-back effectively.

Developments in Finite Element Technology and Optimization Formulations for Sheet Metal Forming

2012 ◽  
pp. 287-318 ◽  
Author(s):  
Robertt A. F. Valente ◽  
Ricardo J. Alves de Sousa ◽  
António Andrade-Campos ◽  
Raquel de-Carvalho ◽  
Marisa P. Henriques ◽  
...  
Keyword(s):  
Finite Element ◽  
Numerical Simulations ◽  
Sheet Metal ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Single Point ◽  
Experimental Result ◽  
Comprehensive Overview ◽  
Plastic Forming ◽  
Main Ideas

This contribution aims to provide a comprehensive overview of some research developments in the field of computational mechanics and numerical simulations applied to metal forming processes. More specifically, this chapter’s goal is to encompass three main fields of research applied to plastic forming processes: (i) the development of alternative finite element formulations for the simulation of sheet metal forming processes; (ii) the development and discussion of distinct optimization procedures and formulations suitable for the characterization of constitutive parameters to be used in numerical simulations, relying on experimental result data; (iii) the study of non-conventional forming processes, particularly the case of single-point incremental forming operations. For each of these topics, a summary of the formulations and main ideas is provided, as well as a list of references for the interested reader. The main goal of this chapter is, therefore, to provide a comprehensive source of information for researchers from both academia and industrial worlds, about some recent achievements and future trends in the numerical simulation field.

Effect of the Constitutive Laws on the Accuracy of Sheet Metal Simulation

2013 ◽  
Vol 535-536 ◽  
pp. 279-283
Author(s):  
Dorel Banabic
Keyword(s):  
Numerical Simulation ◽  
Sheet Metal ◽  
Sheet Metal Forming ◽  
Constitutive Modeling ◽  
Metal Forming ◽  
Steel Sheet ◽  
Plastic Anisotropy ◽  
Advanced Materials ◽  
Sheet Metals ◽  
Simulation Tools

During the last three decades, numerical simulation has gradually extended its applicability in the field of sheet metal forming. Constitutive modeling is one of the domains closely related to the development of numerical simulation tools. The paper is devoted to a comprehensive testing of the advanced materials models as implemented in the finite-element code. The test proves the capability of the advanced materials models response of DC04 steel sheet to describe the effects of the plastic anisotropy of the sheet metals subjected to industrial forming processes.

scholarly journals Single and ensemble classifiers for defect prediction in sheet metal forming under variability

2019 ◽  
Vol 32 (16) ◽  
pp. 12335-12349 ◽  
Author(s):  
M. A. Dib ◽  
N. J. Oliveira ◽  
A. E. Marques ◽  
M. C. Oliveira ◽  
J. V. Fernandes ◽  
...  
Keyword(s):  
Sheet Metal ◽  
Process Parameters ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Sheet Thickness ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Ensemble Classifiers ◽  
Ensemble Models ◽  
Using Data

AbstractThis paper presents an approach, based on machine learning techniques, to predict the occurrence of defects in sheet metal forming processes, exposed to sources of scatter in the material properties and process parameters. An empirical analysis of performance of ML techniques is presented, considering both single learning and ensemble models. These are trained using data sets populated with numerical simulation results of two sheet metal forming processes: U-Channel and Square Cup. Data sets were built for three distinct steel sheets. A total of eleven input features, related to the mechanical properties, sheet thickness and process parameters, were considered; also, two types of defects (outputs) were analysed for each process. The sampling data were generated, assuming that the variability of each input feature is described by a normal distribution. For a given type of defect, most single classifiers show similar performances, regardless of the material. When comparing single learning and ensemble models, the latter can provide an efficient alternative. The fact that ensemble predictive models present relatively high performances, combined with the possibility of reconciling model bias and variance, offer a promising direction for its application in industrial environment.

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