Distribution & Access For Publication Downloads News About Us Contact Us
Paper Titles
Electrocatalytic Activity of Fe-N Doped Carbon Material Derived from Polyaniline-Prussian Blue Composite towards Oxygen Reduction Reaction
p.186
Cathode Performance Degradation of SOFC from Sodium Chloride
p.191
Effect of Al5Ti1B Master Alloy on Microstructure and Mechanical Properties of Al-5wt.%Cu based Alloy
p.195
Electrochemical Characteristics of TiAl Coating on Aluminum Alloy Surface by Supersonic Particles Deposition
p.199
Multiple Regression and Neural Network Based Characterization of Friction in Sheet Metal Forming
p.204
Investigation on the Discharge Behaviors of AZ31 and AZ61 Magnesium Alloys as Anode Material of Metal-Air Fuel Cells
p.211
Ultrasonic and Density Studies of D(+) Mannose with Aqueous Electrolytes at 303K
p.215
Selective Oxidation of Corn Starch in TEMPO-Mediated System
p.221
A Selective Plugging Agent for Horizontal Wells
p.225

Multiple Regression and Neural Network Based Characterization of Friction in Sheet Metal Forming

Article Preview

Abstract:

This article proposes a frictional resistance description approach in sheet metal forming and the objective is to characterize the friction coefficient value under a wide range of friction conditions without performing time-consuming experiments. To describe the friction condition in sheet metal forming simulations, the friction coefficient should be quantified using friction models. Realistic friction models must account for the influence of surface roughness and surface topography on the lubricant flow and dry friction conditions. Due to considerable amount of factors that affect the friction coefficient value, building the analytical friction model for specified process conditions is too demanding. Thus, mathematical models that describe the friction behaviour using multiple regression analysis (MRA) and artificial neural networks (ANN) are utilized. The regression analysis was performed using the user subroutine in the MATLAB, while the ANN model was built in STATISTICA Neural Networks. As input variables for regression model and training of multilayer perceptron (MLP) the results of strip drawing friction test were utilized.

You might also be interested in these eBooks
Applied Engineering Decisions in the Context of Sustainable Development View Preview

Info:

Periodical:

Advanced Materials Research (Volume 1051)

Pages:

204-210

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1051.204

Citation:

Cite this paper

Online since:

October 2014

Authors:

Hirpa G. Lemu*, Tomasz Trzepieciński

Keywords:

Artificial Neural Network (ANN), Coefficient of Friction, Friction, Regression Analysis

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

* - Corresponding Author

References

[1] F. Stachowicz, T. Trzepieciński, Zastosowanie sieci neuronowych do wyznaczania współczynnika tarcia podczas kształtowania blach, Informatyka w Technologii Materiałów 4 (2004) 87-97. (In Polish).

Google Scholar

[2] F. Stachowicz, T. Trzepieciński, ANN application for determination of frictional characteristics of brass sheet metal, J. Artif. Intell 1(2004) 81-90.

Google Scholar

[3] R. Dasgupta, R. Thakur, B. Govindrajan, Regression analysis of factors affecting high stress abrasive wear behaviour, J. of Failure Analysis and Prevention 2 (2002) 65-68.

DOI: 10.1007/bf02715422

Google Scholar

[4] B. Ma, A.K. Tieu, C. Lu, Z. Jiang, A finite-element simulation of asperity flattening in metal forming, J. Mater. Process. Technol 130–131 (2002) 450–455.

DOI: 10.1016/s0924-0136(02)00752-5

Google Scholar

[5] C. Myant, H.A. Spikes, J.R. Stokes, Influence of load and elastic properties on the rolling and sliding friction of lubricated compliant contacts, Tribol. Int 43 (2010) 55–63.

DOI: 10.1016/j.triboint.2009.04.034

Google Scholar

[6] P. Kumar, S.C. Jain, S. Ray, Study of surface roughness effects in elastohydrodynamic lubrication of rolling line contacts using a deterministic model, Tribol. Int 34 (2001) 713–722.

DOI: 10.1016/s0301-679x(01)00066-4

Google Scholar

[7] D.G. Kleinbaum, L.L. Kupper, K.E. Muller, Applied regression analysis and other multivariable methods, PWS Publishing Co., Boston, (1988).

Google Scholar

[8] G. Poshal, P. Ganesan, An analysis of formability of aluminium preforms using neural network, J. Mater. Process. Technol 205 (2005) 272-282.

DOI: 10.1016/j.jmatprotec.2007.11.107

Google Scholar

[9] C. Bruni, A. Forcellese, F. Gabrielli, M. Simoncini, Modelling of the rheological behaviour of aluminium alloys in multistep hot deformation using the multiple regression analysis and artificial neural network techniques, J. Mater. Process. Technol 177 (2006).

DOI: 10.1016/j.jmatprotec.2006.03.230

Google Scholar

[10] W. Li, Manufacturing process diagnosis using functional regression, J. Mater. Process. Technol 186 (2007) 323–330.

Google Scholar

[11] P. Dutta, D.K. Pratihar, Modelling of TIG welding process using conventional regression analysis and neural network-based approaches, J. Mater. Proc. Technol 184 (2007) 56–68.

DOI: 10.1016/j.jmatprotec.2006.11.004

Google Scholar

[12] P.D. Allison, Multiple regression: A primer pine forge press series in research methods and statistics, SAGE Publications Ltd., London, (1999).

Google Scholar

[13] T. Trzepieciński, Metody optymalizacyjne w problemach regresyjnych. Zeszyty Naukowe WSInf. Teoria i Zastosowania Informatyki 11(1) (2012) 100-109. (In polish).

Google Scholar

[14] N.R. Draper, H. Smith, Applied regression analysis, John Wiley and Sons, New York (1998).

Google Scholar

[15] Statistica Neural Networks, Addendum for version 4, StatSoft, Tulsa (1999).

Google Scholar

Scientific.Net is a registered brand of Trans Tech Publications Ltd
© 2024 by Trans Tech Publications Ltd. All Rights Reserved