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Research Papers

Analysis of Torque in Friction Stir Welding of Aluminum Alloy 5052 by Inverse Problem Method

[+] Author and Article Information
Karen Johanna Quintana Cuellar

Department of Mechanical Engineering,
COPPE,
Universidade Federal do Rio de Janeiro,
Rio de Janeiro CEP 21941-972, Brazil;
Centro de Tecnologia,
Cidade Universitária,
Ilha do Fundão, Bloco G,
Sala 204, P.O. Box 68503,
Rio de Janeiro, RJ CEP 21941-972, Brazil
e-mail: kjquintanac@gmail.com

Jose Luis L. Silveira

Mem. ASME
Department of Mechanical Engineering,
COPPE and Escola Politécnica,
Universidade Federal do Rio de Janeiro,
Rio de Janeiro CEP 21941-972, Brazil;
Centro de Tecnologia,
Cidade Universitária,
Ilha do Fundão, Bloco G,
Sala 204, P.O. Box 68503,
Rio de Janeiro, RJ CEP 21941-972, Brazil
e-mail: jluis@mecanica.ufrj.br

1Corresponding author.

Manuscript received June 27, 2016; final manuscript received January 3, 2017; published online January 27, 2017. Assoc. Editor: Wayne Cai.

J. Manuf. Sci. Eng 139(4), 041017 (Jan 27, 2017) (8 pages) Paper No: MANU-16-1350; doi: 10.1115/1.4035719 History: Received June 27, 2016; Revised January 03, 2017

Torque influences the main phenomena that occur during friction stir welding (FSW) process. However, models for torque have received little attention. In this paper, inverse problem method is used to estimate the parameters for a model for torque, measured during FSW experiments for different combinations of rotational and welding speeds. The experimental results are used as input data to estimate the model parameters. The results showed a good agreement between the experimental data and the model obtained using the inverse problem method. The influence of the tool geometry on torque was observed by comparing previously published experimental results and the experimental data presented.

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References

Figures

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Fig. 2

Tool of H13 used to obtain the welds

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Fig. 1

CNC machining center adapted to FSW

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Fig. 6

Comparison between the torque behavior as a function of rotational speed obtained by the Cui's model [24], by the estimated model via inverse problem using experimental data from Ref. [26], and experimental data of torque from literature for different aluminum alloys and welding speeds

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Fig. 3

(a) Experimental data for the torque as a function of the rotational speed and (b) fitted curve of torque for a welding speed of 300 mm/min

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Fig. 4

(a) Reduced sensitivity coefficient for v = 500 mm/min, (b) variable frequency, and (c) fixed frequency of the D-optimal design for different welding speeds in millimeter per minute

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Fig. 5

(a) Convergence of the Levenberg–Marquardt method for the estimated parameters A, C, and a. Parameters converge to A = 3.0222, C = 83.9655, and a = 0.0053. (b) Torque behavior as a function of rotational speed obtained experimentally, by the Cui's model [24] and by the estimated model via inverse problem methodology.

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Fig. 7

Power and specific energy behavior as a function of rotational speed computed from the torque Cui's model [24], from the torque estimated model via inverse problem using the experimental results obtained in this study, and from the torque experimental results

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Fig. 8

Torque, power, and specific energy as a function of the welding speed computed from the estimated model via inverse problem using the present experimental results

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