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.

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.

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.

scholarly journals Cutting Tool Wear Monitoring

2015 ◽  
Vol 15 (3) ◽  
pp. 380-384 ◽  
Author(s):  
Jan Madl ◽  
Michal Martinovsky
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
Tool Wear Monitoring ◽  
Cutting Tool Wear ◽  
Wear Monitoring

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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