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 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 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.

A Machine Vision Approach to Tool Wear Monitoring Based on the Image of Workpiece Surface Texture

2010 ◽  
Vol 154-155 ◽  
pp. 412-416 ◽  
Author(s):  
Zhong Ren Wang ◽  
Yu Feng Zou ◽  
Fan Zhang
Keyword(s):  
Tool Wear ◽  
Machine Vision ◽  
Vision System ◽  
Texture Feature ◽  
Tool Wear Monitoring ◽  
Machined Surface ◽  
Workpiece Surface ◽  
Tool Condition ◽  
Wear Monitoring ◽  
Feature Information

Machine vision technique is an advanced method for tool wear monitoring. In this article, a holding system has been designed and fabricated to realize the combination of machine tools and machine vision system. On-machine experiments were carried out to test the effect of this method. Experimental results indicate that tool condition monitoring can be successfully accomplished by analyzing texture feature information extracted from the machined surface.

scholarly journals A Neuro-Fuzzy Approach Based Tool Condition Monitoring in AISI H13 Milling

2018 ◽  
Author(s):  
Md. Shafiul Alam ◽  
Maryam Aramesh ◽  
Stephen Veldhuis
Keyword(s):  
Tool Wear ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Manufacturing Industry ◽  
Cutting Tool ◽  
Milling Process ◽  
Tool Condition Monitoring ◽  
Tool Wear Monitoring ◽  
Tool Condition ◽  
Wear Monitoring

In the manufacturing industry, cutting tool failure is a probable fault which causes damage to the cutting tools, workpiece quality and unscheduled downtime. It is very important to develop a reliable and inexpensive intelligent tool wear monitoring system for use in cutting processes. A successful monitoring system can effectively maintain machine tools, cutting tool and workpiece. In the present study, the tool condition monitoring system has been developed for Die steel (H13) milling process. Effective design of experiment and robust data acquisition system ensured the machining forces impact in the milling operation. Also, ANFIS based model has been developed based on cutting force-tool wear relationship in this research which has been implemented in the tool wear monitoring system. Prediction model shows that the developed system is accurate enough to perform an online tool wear monitoring system in the milling process.

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.

A Review on Signal Acquisition Methods for Tool Wear Monitoring in Turning Process

2014 ◽  
Vol 984-985 ◽  
pp. 83-93
Author(s):  
D. Rajeev ◽  
D. Dinakaran ◽  
Shanmugam Satishkumar ◽  
Anselm W.A. Lenin
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
Economic Loss ◽  
Signal Acquisition ◽  
Tool Wear Monitoring ◽  
Turning Process ◽  
Tool Condition ◽  
On Line ◽  
Wear Monitoring ◽  
Machine Utilization

On-line monitoring of tool wear in turning is vital to increase machine utilization as scrapped components, machine tool breakage and unscheduled downtime result from worn tool usage cause huge economic loss. Several techniques have been developed for monitoring wear levels on the cutting tool on-line. Keeping in to account the difficulties encountered during the implementation of tool condition monitoring (TCM). The signal acquisition is one of the key elements used during the implementation of TCM. This paper provides an in depth coverage of various signal acquisition methods used in TCM.

A simple approach for on-line tool wear monitoring using the analytic hierarchy process

1997 ◽  
Vol 211 (1) ◽  
pp. 19-27 ◽  
Author(s):  
S Das ◽  
R Islam ◽  
A. B. Chattopadhyay
Keyword(s):  
Tool Wear ◽  
Analytic Hierarchy Process ◽  
Force Measurement ◽  
Tool Wear Monitoring ◽  
Analytic Hierarchy ◽  
Tool Condition ◽  
On Line ◽  
Wear Monitoring ◽  
The Analytic Hierarchy Process ◽  
Hierarchy Process

A wide variety of on-line tool condition monitoring techniques have been developed to the present time. Timely decision making for cutting tool indexing needs a proper method for assessment of the state of the tool on-line. The present work demonstrates a very simple system based on cutting force measurement for determination of the tool condition on-line using the analytic hierarchy process (AHP). The technique shows reasonably close estimation of the tool condition and enables successful on-line tool wear monitoring.

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