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

2015 ◽  
Vol 138 (5) ◽  
Author(s):  
Chenhui Shao ◽  
Tae Hyung Kim ◽  
S. Jack Hu ◽  
Jionghua (Judy) Jin ◽  
Jeffrey A. Abell ◽  
...  
Keyword(s):  
Tool Wear ◽  
Measurement Data ◽  
Lithium Ion ◽  
Production Costs ◽  
Tool Wear Monitoring ◽  
Surface Geometry ◽  
Ultrasonic Metal Welding ◽  
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 monitoring algorithm using a quadratic classifier and features that are extracted from space and frequency domains of cross-sectional profiles on tool surfaces. The developed algorithm is validated using tool measurement data from a battery plant.

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.

Tool Wear Monitoring and Alarm System Based on Pattern Recognition With Logical Analysis of Data

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Yasser Shaban ◽  
Soumaya Yacout ◽  
Marek Balazinski
Keyword(s):  
Pattern Recognition ◽  
Tool Wear ◽  
Proportional Hazards Model ◽  
Optimization Technique ◽  
Logical Analysis ◽  
Alarm System ◽  
Logical Analysis Of Data ◽  
Tool Wear Monitoring ◽  
Wear Monitoring ◽  
Tool Replacement

This paper presents a new tool wear monitoring and alarm system that is based on logical analysis of data (LAD). LAD is a data-driven combinatorial optimization technique for knowledge discovery and pattern recognition. The system is a nonintrusive online device that measures the cutting forces and relates them to tool wear through learned patterns. It is developed during turning titanium metal matrix composites (TiMMCs). These are a new generation of materials which have proven to be viable in various industrial fields such as biomedical and aerospace. Since they are quite expensive, our objective is to increase the tool life by giving an alarm at the right moment. The proposed monitoring system is tested by using the experimental results obtained under sequential different machining conditions. External and internal factors that affect the turning process are taken into consideration. The system's alarm limit is validated and is compared to the limit obtained when the statistical proportional hazards model (PHM) is used. The results show that the proposed system that is based on using LAD detects the worn patterns and gives a more accurate alarm for cutting tool replacement.

Development of Wear Sensor for Tool Management System

1988 ◽  
Vol 110 (1) ◽  
pp. 59-62 ◽  
Author(s):  
G. Rutelli ◽  
D. Cuppini
Keyword(s):  
Tool Wear ◽  
Computer System ◽  
Tool Wear Monitoring ◽  
Tool Management ◽  
Replacement Strategy ◽  
Cnc Lathe ◽  
Wear Monitoring ◽  
Tool Replacement ◽  
Automatic Tool ◽  
And Performance

In automatic metalworking systems, in-process tool-life monitoring and quality control of the parts produced play a crucial role. This paper is on the architecture and performance of an opto-electronic sensor designed for automatic tool-wear monitoring in Computer Numerical Controlled (CNC) lathe applications. Tool wear is sensed by detecting the wear land image, which is captured by an analogic camera, digitized and processed using a computer system. The computer system, linked to the lathe control module, implements a real-time procedure supporting an optimal tool replacement strategy.

scholarly journals Tool Wear Monitoring in Turning: A Neural Network Application

2001 ◽  
Vol 34 (7) ◽  
pp. 207-222 ◽  
Author(s):  
Bernhard Sick
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Tool Wear ◽  
Process Parameters ◽  
Production Costs ◽  
Cutting Conditions ◽  
Tool Wear Monitoring ◽  
Computing Technique ◽  
Wear Monitoring ◽  
Signal Changes

Tool wear monitoring is the most difficult task in the area of tool condition monitoring for metal-cutting manufacturing processes. The main objective is to improve the process reliability, but the production costs need to be reduced as well. This article summarises a new approach for online and indirect tool wear estimation or classification in turning using neural networks. This technique uses a physical process model describing the influence of cutting conditions (such as the feed rate) on measured process parameters (here: cutting force signals) in order to separate signal changes caused by variable cutting conditions from signal changes caused by tool wear. Features extracted from the normalised process parameters are taken as inputs of a dynamic, but nonrecurrent neural network that estimates the current state of the tool. It is shown that the estimation error can be reduced significantly with this combination of a hard computing and a soft computing technique. The article represents an extended summary of the author's investigations and publications in the area of online and indirect tool wear monitoring in turning by means of artificial neural networks.

Tool wear monitoring using acoustic emission in the existence of chatter

1990 ◽  
Vol 28 (10) ◽  
pp. 1861-1869 ◽  
Author(s):  
YOICHI MATSUMOTO ◽  
NGUN TJIANG ◽  
BOBBIE FOOTE ◽  
YNGVE NAERHEIMH
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Tool Wear Monitoring ◽  
Wear Monitoring
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