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contributor authorXi, Liang
contributor authorBanu, Mihaela
contributor authorJack Hu, S.
contributor authorCai, Wayne
contributor authorAbell, Jeffrey
date accessioned2017-11-25T07:17:34Z
date available2017-11-25T07:17:34Z
date copyright2016/10/8
date issued2017
identifier issn1087-1357
identifier othermanu_139_01_011008.pdf
identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4234656
description abstractUltrasonic metal welding has been used to join multiple layers of battery tabs with the bus bar in lithium-ion battery assembly operations. This paper describes joint performance models for ultrasonic metal welds of multiple layers of dissimilar battery tab materials, i.e., aluminum and copper. Finite element (FE) models are developed to predict the mechanical performance of the ultrasonically welded joints. The models predict peak shear load, energy absorption capability, and failure modes, which are necessary for modeling product performance and defining process requirements for the welds. The models can be adjusted to represent different quality of welds created in conditions of underweld (UW), normal-weld (NW), or overweld (OW) using physical attributes observed through microscopic analysis. The models are validated through lap shear tests, which demonstrate excellent agreement for the maximum force in the NW condition and good agreement for the UW and OW conditions. The models provide in-depth understanding of the relationship among welding process parameters, physical weld attributes, and the weld performance. The models also provide significant insight for further development of ultrasonic welding process for battery tabs and help optimize welding process for more than four-layered joints.
publisherThe American Society of Mechanical Engineers (ASME)
titlePerformance Prediction for Ultrasonically Welded Dissimilar Materials Joints
typeJournal Paper
journal volume139
journal issue1
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.4033692
journal fristpage11008
journal lastpage011008-13
treeJournal of Manufacturing Science and Engineering:;2017:;volume( 139 ):;issue: 001
contenttypeFulltext


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