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Indexed by:期刊论文

Date of Publication:2017-12-01

Journal:JOURNAL OF MANUFACTURING PROCESSES

Included Journals:Scopus、SCIE、EI

Volume:30

Page Number:63-74

ISSN No.:1526-6125

Key Words:Ultrasonic welding; Aluminum alloys; Steel; Welding parameter; Artificial neural network

Abstract:Aluminum and steel are widely used in automotive and aerospace industries. 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 aluminum-steel dissimilar alloys to investigate the influences of welding parameters on joint strength. Designed and conducted a 3-factor, 3-level comprehensive test. The analyses of test results show that there are 3 kinds of fractures on the welding joint with different welding parameters. The highest strength can reach 3910 N. Clamping force and vibration amplitude not significantly impact the tensile strength. Vibration time significantly impact the tensile strength although its significance level is close to the threshold. The interaction between welding parameters all can significantly impact the tensile strength. The artificial neural network optimized by Genetic Algorithm was used to establish an analytical model. The supplemental experiment and residual analysis were conducted to verify the accuracy of the analytical model. The analytical model show that with the increase of clamping force, the changes of optimal and minimum strength are limited, but the range of welding parameters to obtain a higher strength change significantly; the optimal welding parameters from lower vibration amplitude and higher vibration time shifts towards to higher vibration amplitude and shorter vibration time gradually; for 0.3 Mpa clamping force, the influences of vibration amplitude and vibration time on tensile strength are not significant. (C) 2017 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.