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Ultrasonic Welding of Magnesium-Titanium Dissimilar Metals: A Study on Influences of Welding Parameters on Mechanical Property

[+] Author and Article Information
Dewang Zhao

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

Kunmin Zhao

School of Automotive Engineering, Dalian University of Technology; Institute of Industrial and Equipment Technology, Hefei University of Technology, 2 Ling Gong Road, Dalian, 116024, China
kmzhao@dlut.edu.cn

Daxin Ren

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

X.L. Guo

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

1Corresponding author.

ASME doi:10.1115/1.4035539 History: Received June 06, 2016; Revised December 12, 2016

Abstract

The advancement in application of light alloys such as magnesium and titanium are closely related to the state-of-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 analyses of variance was adopted to study the weight of each welding parameter and their interactions. The artificial neural network 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 of vibration time, the increase of 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 expanding gradually and reach maximum when the vibration time is 1000 ms, then shifts towards the low end gradually.

Copyright (c) 2016 by ASME
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