Effect of the interface characteristics on the joint properties and diffusion mechanisms during ultrasonic metal welding of Al/Cu

2017 ◽  
Vol 61 (3) ◽  
pp. 499-506 ◽  
Author(s):  
Jean Pierre Bergmann ◽  
Anna Regensburg ◽  
René Schürer ◽  
Franziska Petzoldt ◽  
Alexander Herb
Keyword(s):  
Ultrasonic Metal Welding ◽  
Joint Properties ◽  
Interface Characteristics ◽  
Diffusion Mechanisms ◽  
Metal Welding ◽  
And Diffusion

Applications of Micro Sensors for Fundamental Study of Manufacturing Processes

2007 ◽  
Author(s):  
Xudong Cheng ◽  
Hongseok Choi ◽  
Xiaochun Li
Keyword(s):  
Harsh Environment ◽  
Manufacturing Processes ◽  
Film Sensor ◽  
Fundamental Study ◽  
Metallic Material ◽  
Ultrasonic Metal Welding ◽  
University Of Wisconsin ◽  
Fundamental Understanding ◽  
Metal Welding ◽  
And Diffusion

Fundamental understanding of many multiscale manufacturing processes can be significantly improved with better sensing and instrumentation techniques. In light of the recent advances in micro senor technologies, this paper first reviews the recent research conducted at University of Wisconsin-Madison in developing methods to integrate micro thin film sensor into harsh environment manufacturing processes. The study demonstrates how thermomechanical data obtained with distributed micro sensors to improve the understanding of laser micromachining and mesoscale ultrasonic metal welding. The paper then presents the current study in an effort to extend the method to more manufacturing process, the feasibility of embedded micro sensors in metallic material using ultrasonic welding and diffusion bonding was investigated.

scholarly journals 3D finite element model of dynamic material behaviors for multilayer ultrasonic metal welding

2021 ◽  
Vol 62 ◽  
pp. 302-312
Author(s):  
Ninggang Shen ◽  
Avik Samanta ◽  
Wayne W. Cai ◽  
Teresa Rinker ◽  
Blair Carlson ◽  
...  
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Element Model ◽  
3D Finite Element ◽  
Ultrasonic Metal Welding ◽  
3D Finite Element Model ◽  
Material Behaviors ◽  
Metal Welding

Li+ionic conductivities and diffusion mechanisms in Li-based imides and lithium amide

2012 ◽  
Vol 14 (5) ◽  
pp. 1596-1606 ◽  
Author(s):  
Wen Li ◽  
Guotao Wu ◽  
Zhitao Xiong ◽  
Yuan Ping Feng ◽  
Ping Chen
Keyword(s):  
Ionic Conductivities ◽  
Lithium Amide ◽  
Diffusion Mechanisms ◽  
And Diffusion

Process Monitoring in Ultrasonic Metal Welding of Lithium Batteries by Power Signals

2021 ◽  
pp. 1-22
Author(s):  
Xinhua Shi ◽  
Lin Li ◽  
Suiran Yu ◽  
Lingxiang Yun
Keyword(s):  
Electric Power ◽  
Process Monitoring ◽  
Lithium Batteries ◽  
Welding Process ◽  
Ultrasonic Metal Welding ◽  
Isometric Transformation ◽  
Welding Defects ◽  
Different Types ◽  
Metal Welding ◽  
Welding Processes

Abstract Ultrasonic metal welding is one of the key technologies in manufacturing lithium batteries, and the welding quality directly determines the battery performance. Therefore, an online welding process monitoring system is critical in identifying abnormal welding processes, detecting defects, and improving battery quality. Traditionally, the peak welding power is used to indicate abnormal process signals in welding process monitoring systems. However, since various factors have complex impacts on the electric power signals of ultrasonic welding processes, the peak power is inadequate to detect different types of welding defects. Therefore, a signal pattern matching method is proposed in this study, which is based on the electric power signal during the entire welding process and thus is capable of identifying abnormal welding processes in various conditions. The proposed method adopts isometric transformation and homogenization as signal pretreatment methods, and Euclidean distance is used to calculate the similarity metric for signal matching. The effectiveness and robustness of the proposed method are experimentally validated under different abnormal welding conditions.

THE STABILITY OF LOW INDEX METAL SURFACES TO TOPOLOGICAL DEFECTS

1991 ◽  
Vol 05 (03) ◽  
pp. 427-459 ◽  
Author(s):  
EDWARD H. CONRAD
Keyword(s):  
Metal Surfaces ◽  
Surface Defects ◽  
Defect Formation ◽  
Fundamental Problem ◽  
Topological Defects ◽  
Surface Roughening ◽  
Thermal Generation ◽  
Diffusion Mechanisms ◽  
The Stability ◽  
And Diffusion

The study of defect formation at metal surfaces is a fundamental problem in surface physics. An understanding of defect formation is pertinent to growth and diffusion mechanisms. In addition, surface roughening, faceting, and surface melting are all defect mediated phase transitions involving the formation of different topological defects. While the importance of defects at surfaces is well recognized, the study of surface defects has been hampered by the lack of sufficiently accurate experimental techniques. In fact, it is only in the past 6 years that experiments on the thermal generation of defects on metal surfaces have been performed. This review attempts to outline both the theoretical and experimental work on surface defect formation on metal systems.

Tool Wear Monitoring for Ultrasonic Metal Welding of Lithium-Ion Batteries

2015 ◽  
Author(s):  
Chenhui Shao ◽  
Tae Hyung Kim ◽  
S. Jack Hu ◽  
Jionghua (Judy) Jin ◽  
Jeffrey A. Abell ◽  
...  
Keyword(s):  
Tool Wear ◽  
Lithium Ion ◽  
Production Costs ◽  
Tool Wear Monitoring ◽  
Surface Geometry ◽  
Ultrasonic Metal Welding ◽  
Tool Condition ◽  
Wear Monitoring ◽  
Tool Replacement ◽  
Metal Welding

This paper presents a tool wear monitoring framework for ultrasonic metal welding which has been used for lithium-ion battery manufacturing. Tool wear has a significant impact on joining quality. In addition, tool replacement, including horns and anvils, constitutes an important part of production costs. Therefore, a tool condition monitoring (TCM) system is highly desirable for ultrasonic metal welding. However, it is very challenging to develop a TCM system due to the complexity of tool surface geometry and a lack of thorough understanding on the wear mechanism. Here, we first characterize tool wear progression by comparing surface measurements obtained at different stages of tool wear, and then develop a tool condition classification algorithm to identify the state of wear. The developed algorithm is validated using tool measurement data from a battery plant.

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