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.

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.

Study of Tool Wear Monitoring Using Machine Vision

2020 ◽  
Vol 54 (3) ◽  
pp. 259-270
Author(s):  
Ruitao Peng ◽  
Haolin Pang ◽  
Haojian Jiang ◽  
Yunbo Hu
Keyword(s):  
Tool Wear ◽  
Machine Vision ◽  
Tool Wear Monitoring ◽  
Wear Monitoring

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.

Tool Wear Monitoring: An Automated System Based on Multiple Cutting Force Parameters and Machine Vision Technique

2009 ◽  
Vol 626-627 ◽  
pp. 5-10 ◽  
Author(s):  
Yu Teng Liang ◽  
Yih Chih Chiou
Keyword(s):  
Tool Wear ◽  
Machine Vision ◽  
Cutting Force ◽  
Monitoring System ◽  
Flank Wear ◽  
Cutting Parameters ◽  
Tool Wear Monitoring ◽  
Model Based ◽  
Wear Monitoring ◽  
Vision Technique

This study proposes a tool wear automatic monitoring system based on multiple parameters analysis of cutting force and machine vision technique. A drilling model of cutting parameters (cutting force, coating layer, spindle speed and feed rate) and tool condition (focusing on tool flank wear measurement and analysis) was developed. The experimental design methods developed in this study can be used to optimize cutting parameters efficiently and reliably. The drilling model based on cutting parameters was constructed using Taguchi method. This method enabled evaluation of wear status based on the actual force obtained from a dynamometer. The derived relation is useful for in-process wear monitoring. Tool wear dynamics are extremely complex and not yet fully understood. Therefore, vision-based tool wear monitoring techniques can help elucidate wear progression. In this study, a drilling model based on the machine vision technique was used to establish a direct relation between cutting parameters and tool wear. The object of the experiments was to measure the flank wear of cutting tools with various coatings. The experimental results show that the monitoring system clarifies the relationships between cutting force and multiple cutting parameters.

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 An Improved Tool Wear Monitoring Method Using Local Image and Fractal Dimension of Workpiece

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haicheng Yu ◽  
Kun Wang ◽  
Ruhai Zhang ◽  
Xiaojun Wu ◽  
Yulin Tong ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Tool Wear ◽  
Surface Texture ◽  
High Reliability ◽  
Machining Process ◽  
Fast Fourier Transformation ◽  
Tool Wear Monitoring ◽  
Monitoring Method ◽  
Workpiece Surface ◽  
Wear Monitoring

Tool wear is a key factor that dominates the surface quality and distinctly influences the generated workpiece surface texture. In order to realize accurate evaluation of the tool wear from the generated workpiece surface after machining process, a new tool wear monitoring method is developed by fractal dimension of the acquired workpiece surface digital image. A self-made simple apparatus is employed to capture the local digital images around the region of interest. In addition, a skew correction method based on local fast Fourier transformation energy is also proposed for the surface texture direction adjustment. Furthermore, the tool wear quantitative evaluation was derived based on fractal dimension utilizing its high reliability for inherent irregularity description. The proposed tool wear monitoring method has verified its feasibility as well as its effectiveness in actual milling experiments using the material of AISI 1045 in a vertical machining center. Testing results demonstrate that the proposed method was capable of tool wear condition evaluation.

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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