Research Papers

Ultrasonic Welding of Magnesium–Titanium Dissimilar Metals: A Study on Influences of Welding Parameters on Mechanical Property by Experimentation and Artificial Neural Network

[+] Author and Article Information
Dewang Zhao

State Key Laboratory of Structural
Analysis for Industrial Equipment,
Department of Engineering Mechanics,
Dalian University of Technology,
2 Ling Gong Road,
Dalian 116024, China
e-mail: dewangzhao@yahoo.com

Kunmin Zhao

School of Automotive Engineering,
Dalian University of Technology,
Dalian 116024, China;
Institute of Industrial and Equipment Technology,
Hefei University of Technology,
Hefei 230601, China
e-mail: kmzhao@dlut.edu.cn

Daxin Ren

Key Laboratory of Liaoning Advanced
Welding and Joining Technology,
School of Automotive Engineering,
Dalian University of Technology,
2 Ling Gong Road,
Dalian 116024, China
e-mail: rendx@dlut.edu.cn

Xinglin Guo

State Key Laboratory of Structural
Analysis for Industrial Equipment,
Department of Engineering Mechanics,
Dalian University of Technology,
2 Ling Gong Road,
Dalian 116024, China
e-mail: xlguo@dlut.edu.cn

1Corresponding author.

Manuscript received June 6, 2016; final manuscript received December 12, 2016; published online January 27, 2017. Assoc. Editor: Wayne Cai.

J. Manuf. Sci. Eng 139(3), 031019 (Jan 27, 2017) (9 pages) Paper No: MANU-16-1321; doi: 10.1115/1.4035539 History: Received June 06, 2016; Revised December 12, 2016

The advancement in the application of light alloys such as magnesium and titanium is closely related to the state of the art of joining them. As a new type of solid-phase welding, ultrasonic spot welding is an effective way to achieve joints of high strength. In this paper, ultrasonic welding was carried out on magnesium–titanium dissimilar alloys to investigate the influences of welding parameters on joint strength. The analysis of variance was adopted to study the weight of each welding parameter and their interactions. The artificial neural network (ANN) was used to predict joint strength. Results show that in ultrasonic welding of magnesium and titanium alloys, clamping force is the most significant factor, followed by vibration time and vibration amplitude; the interactions between vibration time and vibration amplitude, and between vibration amplitude and clamping force also significantly impact the strength. By using the artificial neural network, test data were trained to obtain a high precision network, which was used to predict the variations of joint strength under different parameters. The analytical model predicts that with the increase in vibration time, the increase in optimal joint strength is limited, but the range of welding parameters to obtain a higher joint strength increases significantly; the minimum joint strength increases as well; and the optimal vibration amplitude expands gradually and reaches the maximum when the vibration time is 1000 ms, then shifts toward the low end gradually.

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Fig. 1

Schematic diagram of ultrasonic welding and specimen dimension

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Fig. 2

Approximate weldment for tensile-shear test

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Fig. 3

The shape of joint fracture

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Fig. 4

Three-dimensional plot of joint strength

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Fig. 5

Main effect plots and interaction plot for tensile strength

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Fig. 6

Flow chart for the GA-ANN algorithm

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Fig. 7

Network topology diagram

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Fig. 8

Analysis of regression between experimental values and predicted values

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Fig. 9

The residual analysis result

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Fig. 10

The distribution of joint strength under different vibration time: (a) change in joint strength under vibration time of 600 ms, (b) change in joint strength under vibration time of 800 ms, (c) change in joint strength under vibration time of 1000 ms, and (d) change in joint strength under vibration time of 1300 ms



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