Effects of Thickness Combinations on Joint Properties and Process Windows in Ultrasonic Metal Welding

2009 ◽  
Author(s):  
N. W. Wright ◽  
J. D. Robson ◽  
P. B. Prangnell
Keyword(s):  
Ultrasonic Metal Welding ◽  
Joint Properties ◽  
Metal Welding

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

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

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.

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.

Ultrasonic Rapid Manufacturing: Implementation and Thermo-Mechanical Analysis

2002 ◽  
Author(s):  
Shailendra Yadav ◽  
Charalabos Doumanidis
Keyword(s):  
Temperature Effects ◽  
Welding Process ◽  
Mechanical Analysis ◽  
Ultrasonic Welding ◽  
Solid Metal ◽  
Rapid Manufacturing ◽  
Mechanical Process ◽  
Ultrasonic Metal Welding ◽  
Metal Parts ◽  
Metal Welding

This paper addresses a novel non-thermal Ultrasonic Rapid Manufacturing (URM), for layered parts based on Ultrasonic Metal Welding (USW). Its laboratory implementation, automation and integration are described first. The thermo-mechanical process aspects (i.e. heat generation and resulting temperature effects) during each cycle of ultrasonic welding are then studied. The technical advantages of ultrasonic welding process, including fabrication of dense, full-strength functional solid metal parts, multi-material composites, and active parts with embedded intelligent components and electronic, mechatronic, optic and fluidic structures, are examined.

scholarly journals Profile Monitoring and Fault Diagnosis Via Sensor Fusion for Ultrasonic Welding

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Weihong (Grace) Guo ◽  
Jionghua (Judy) Jin ◽  
S. Jack Hu
Keyword(s):  
Real Data ◽  
Ultrasonic Welding ◽  
Profile Data ◽  
Ultrasonic Metal Welding ◽  
Monitoring And Diagnosis ◽  
Operational Quality ◽  
Vector Projection ◽  
Sensor Signals ◽  
Rich Information ◽  
Metal Welding

Sensor signals acquired during the manufacturing process contain rich information that can be used to facilitate effective monitoring of operational quality, early detection of system anomalies, and quick diagnosis of fault root causes. This paper develops a method for effective monitoring and diagnosis of multisensor heterogeneous profile data based on multilinear discriminant analysis. The proposed method operates directly on the multistream profiles and then extracts uncorrelated discriminative features through tensor-to-vector projection, and thus, preserving the interrelationship of different sensors. The extracted features are then fed into classifiers to detect faulty operations and recognize fault types. The developed method is demonstrated with both simulated and real data from ultrasonic metal welding.

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