Milling Tool Wear Condition Monitoring Based on Extension Theory

2012 ◽  
Vol 562-564 ◽  
pp. 523-527
Author(s):  
Dong Min Wu ◽  
Ming Shen
Keyword(s):  
Tool Wear ◽  
Vibration Signal ◽  
Extension Theory ◽  
Milling Force ◽  
Condition Evaluation ◽  
Milling Tool ◽  
Force Signal ◽  
Ae Signal ◽  
Wear Monitoring ◽  
Wear Condition

The method of mill tool wear condition evaluation based on the extension theory is put forward by the paper. Through researching, the author firstly designs mill tool wear monitoring system which can acquire milling force signal, AE signal, vibration signal and main motor power signal. Based on the matter-element of classics, joint domain and evaluation, and deducting the dependent functions, at last the objective and reasonable evaluation results are got. It is proved that the extension theory is the valid and reliable in evaluating the mill tool wear condition.

Study of AE Signal for Tool Wear Monitoring in Micro Milling Based on LVQ Algorithm

2010 ◽  
Vol 126-128 ◽  
pp. 719-725 ◽  
Author(s):  
Chia Liang Yen ◽  
Ming Chyuan Lu ◽  
Jau Liang Chen
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Frequency Domain ◽  
Micro Milling ◽  
Tool Wear Monitoring ◽  
Characteristic Analysis ◽  
Classification Rate ◽  
Ae Signal ◽  
Wear Monitoring ◽  
Wear Condition

The Acoustic Emission signal was studied in this report for tool wear monitoring in micro milling. An experiment was conducted first to collect the AE signal generated from the workpiece during cutting process for characteristic analysis, training the system model and finally testing the system performance. In the system development, Acoustic Emission (AE) signals were first transformed to the frequency domain with different feature bandwidth, and then the Learning Vector Quantization (LVQ) algorithms was adopted for classifying the tool wear condition based on the generated AE spectral features. The results show that the frequency domain signal provides the better characteristics for monitoring tool wear condition than the time domain signal. In considering the capability of the AE signal combined with LVQ algorithms, the sharp tool condition can be detected successfully. At the same time, 80% to 95% of the classification rate can be obtained in this study for the worn tool test. Moreover, the increase of the feature bandwidth improved the classification rate for the worn tool case and 95% of classification rate for the case with 10 kHz feature bandwidth.

Research on Tool Wear Form in Micro Turn-Milling Process

2012 ◽  
Vol 184-185 ◽  
pp. 663-667 ◽  
Author(s):  
Lin Hui Zhao ◽  
Jian Cheng Zhang ◽  
Wei Su
Keyword(s):  
Tool Wear ◽  
Small Diameter ◽  
Milling Process ◽  
Milling Tool ◽  
Key Factor ◽  
Part Surface ◽  
End Mills ◽  
Series Of Experiments ◽  
Turn Milling ◽  
Wear Condition

In micro machining, turn-milling tool wear is a key factor for part surface quality. This paper carries on experiments on end mills wear in micro turn-milling machining, aiming to research the wear form and provide some reference data for developing wear standard of small diameter end mills. To measure wear condition of end mills, machine vision technique is utilized. This paper designs and sets up an online end mill wear measurement system for a micro turn-milling process center. With a series of experiments on small diameter end mills, wear conditions of different cutting positions are researched. Based on analysis of experiment data, wear characteristics and wear rule for micro turn-milling process are summarized in this paper.

On-Line Monitoring the Hard Turning Using Distribution Parameters of Acoustic Emission Signal

2015 ◽  
Vol 787 ◽  
pp. 907-911
Author(s):  
J. Bhaskaran
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Hard Turning ◽  
Metal Cutting ◽  
Cubic Boron Nitride ◽  
Emission Signal ◽  
Distribution Parameters ◽  
On Line ◽  
Ae Signal ◽  
Wear Monitoring

In hard turning, tool wear of cutting tool crossing the limit is highly undesirable because it adversely affects the surface finish. Hence continuous, online tool wear monitoring during the process is essential. The analysis of Acoustic Emission (AE) signal generated during conventional machining has been studied by many investigators for understanding the process of metal cutting and tool wear phenomena. In this experimental study on hard turning, the skew and kurtosis parameters of root mean square values of AE signal (AERMS) have been used for online monitoring of a Cubic Boron Nitride (CBN) tool wear.

Face Milling Tool Wear Condition Monitoring Based on Wavelet Transform and Shannon Entropy

2014 ◽  
Vol 541-542 ◽  
pp. 1419-1423 ◽  
Author(s):  
Min Zhang ◽  
Hong Qi Liu ◽  
Bin Li
Keyword(s):  
Wavelet Transform ◽  
Tool Wear ◽  
Condition Monitoring ◽  
Shannon Entropy ◽  
Spindle Motor ◽  
Face Milling ◽  
Motor Current ◽  
Milling Tool ◽  
Spindle Motor Current ◽  
Wear Condition

Tool condition monitoring is an important issue in the advanced machining process. Existing methods of tool wear monitoring is hardly suitable for mass production of cutting parameters fluctuation. In this paper, a new method for milling tool wear condition monitoring base on tunable Q-factor wavelet transform and Shannon entropy is presented. Spindle motor current signals were recorded during the face milling process. The wavelet energy entropy of the current signals carries information about the change of energy distribution associated with different tool wear conditions. Experiment results showed that the new method could successfully extract significant signature from the spindle-motor current signals to effectively estimate tool wear condition during face milling.

Morphological Filtering Algorithm for Estimation of Tool Wear in CNC Milling Machining

2011 ◽  
Vol 697-698 ◽  
pp. 566-569
Author(s):  
Qian Ning ◽  
Tai Yong Wang
Keyword(s):  
Tool Wear ◽  
Manufacturing System ◽  
Systematic Approach ◽  
Vibration Signal ◽  
Cnc Milling ◽  
Tool Condition ◽  
Morphological Filtering ◽  
Force Signal ◽  
Tool Wear Estimation ◽  
Feature Values

Estimation of tool condition has very important meaning to improve the product quality, continuous machining ability and reliability of the manufacturing system. Based on mathematical morphology, a systematic approach is developed to implement online estimation of tool wear in this paper. As the nonlinear filter, morphological filter is selected to reduce the higher frequency noises before feature values extraction. The feature vector consists of original characteristics of vibration signal and cutting force signal. Then, they are input into SVM for training and testing. Experiments show that this method can achieve tool wear estimation effectively.

scholarly journals Milling Force Coefficients-based Tool Wear Monitoring for Variable Parameters Milling

2021 ◽  
Author(s):  
Tianhang Pan ◽  
Jun Zhang ◽  
Xing Zhang ◽  
Wanhua Zhao ◽  
Huijie Zhang ◽  
...  
Keyword(s):  
Tool Wear ◽  
Support Vector ◽  
Machining Accuracy ◽  
Force Model ◽  
Machining Parameters ◽  
Tool Wear Monitoring ◽  
Milling Force ◽  
Force Coefficients ◽  
Milling Parameters ◽  
Wear Monitoring

Abstract Tool wear is an important factor that affects the aeronautical structural parts' quality and machining accuracy in the milling process. It is essential to monitor the tool wear in titanium alloy machining. The traditional tool wear features such as root mean square (RMS), kurtosis, and wavelet packet energy spectrum are related to not only the tool wear status but also to the milling parameters, thus monitoring the tool wear status only under fixed milling parameters. This paper proposes a new method of online monitoring of tool wear using milling force coefficients. The instantaneous cutting force model is used to extract the milling force coefficients which are independent of milling parameters. The principal component analysis (PCA) algorithm is used to fuse the milling force coefficients. Furthermore, support vector machine (SVM) model is used to monitor tool wear states. Experiments with different machining parameters were conducted to verify the effectiveness of this method used for tool wear monitoring. The results show that compared to traditional features, the milling force coefficients are not dependent on the milling parameters, and using milling force coefficients can effectively monitor the transition point of cutters from normal wear to severe wear (tool failure).

Study of Micro-Tool Wear Based on Finite Element Method

2011 ◽  
Vol 314-316 ◽  
pp. 1806-1810
Author(s):  
Jin Feng Zhang ◽  
Ya Dong Gong ◽  
Yue Ming Liu ◽  
Jun Cheng ◽  
Xue Long Wen
Keyword(s):  
Tool Wear ◽  
Micro Milling ◽  
Operation Condition ◽  
Wear Model ◽  
Milling Force ◽  
Edge Radius ◽  
Tool Edge ◽  
Milling Tool ◽  
Milling Operation ◽  
Micro Tool

This paper presents mechanisms studies of micro scale milling operation focusing on micro cutter edge radius. To address this issue, the tool wear model is developed in the present work. Starting with an analysis of the milling edge radius of the tool, the influence of the downscaling on the edge radius is determined by analyzing the milling force. This simulation is used to predict the extent of tool wear in the same milling operation condition by increasing of the tool edge radius. This model accurately predicted the increasing of force with tool wear progress and provides the means for further study of the micro milling tool wear.

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