scholarly journals Applicability of Tool Condition Monitoring Methods Used for Conventional Milling in Micromilling: A Comparative Review

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Soumen Mandal
Keyword(s):  
Condition Monitoring ◽  
Cutting Tools ◽  
Milling Process ◽  
Tool Condition Monitoring ◽  
Research Trend ◽  
Monitoring Methods ◽  
Nose Radius ◽  
Continuous Contact ◽  
Tool Condition ◽  
Comparative Review

Micromilling is a contact based material removal process in which a rotating tool with nose radius in microns is fed over a stationary workpiece. In the process small amount of material gets chipped off from the workpiece. Due to continuous contact between tool and workpiece significant damage occurs to the cutting tools. Mitigating tool damage to make micromilling systems more reliable for batch production is the current research trend. In macroscale or conventional milling process a number of methods have been proposed for tool condition monitoring. Few of them have been applied for micromilling. This paper reviews different methods proposed and used in last two decades for monitoring the condition of micromilling tools. Applicability of tool condition monitoring methods used in conventional milling has been compared with the similar ones proposed for micromilling. Further, the challenges and opportunities on the applicability issues have been discussed.

scholarly journals Tool Condition Monitoring in Milling Process Based on Multi-Dource Pattern Recognition Model

2021 ◽  
Author(s):  
Kui Liang ◽  
Wei Dai ◽  
Tingting Huang ◽  
Zhiyuan Lu
Keyword(s):  
Pattern Recognition ◽  
Condition Monitoring ◽  
Milling Process ◽  
Tool Condition Monitoring ◽  
Monitoring Methods ◽  
Model Framework ◽  
Recognition Model ◽  
Practical Applications ◽  
Transfer Path ◽  
Tool Condition

Abstract In the milling process of metallic parts, appropriate tool condition is essential to reducing processing faults and ensuring manufacturing quality. However, the existing condition monitoring methods are usually limited by recognizing intermediate abnormal states in milling processing, which is inefficient and impractical for real practical applications. Therefore, this paper proposes a Tool condition monitoring (TCM) method in milling process based on multi-source pattern recognition and state transfer path. Firstly, improved K-Means clustering method is used to generate multiple patterns of tool wear. Secondly, a multi-source pattern recognition model framework is developed, and the multiple observation windows and the pattern transfer path are considered in multi-source pattern recognition model. Lastly, PHM2010 datasets are used to verify the feasibility of the proposed method, and the results demonstrate the applicability of the proposed method in practice for tool condition monitoring.

Tool condition monitoring in the milling process with vegetable based cutting fluids using vibration signatures

2019 ◽  
Vol 61 (3) ◽  
pp. 282-288 ◽  
Author(s):  
Thangamuthu Mohanraj ◽  
Subramaniam Shankar ◽  
Rathanasamy Rajasekar ◽  
Ramasamy Deivasigamani ◽  
Pallakkattur Muthusamy Arunkumar
Keyword(s):  
Condition Monitoring ◽  
Milling Process ◽  
Tool Condition Monitoring ◽  
Cutting Fluids ◽  
Tool Condition ◽  
Vibration Signatures

Acoustic Emission Signal Analysis for Tool Condition Monitoring in Microturning of Titanium Alloy

2014 ◽  
Vol 984-985 ◽  
pp. 31-36 ◽  
Author(s):  
A. Gopikrishnan ◽  
A.K. Nizamudheen ◽  
M. Kanthababu
Keyword(s):  
Acoustic Emission ◽  
Titanium Alloy ◽  
Tool Wear ◽  
Condition Monitoring ◽  
Dominant Frequency ◽  
Tool Condition Monitoring ◽  
Discrete Wavelet ◽  
Nose Radius ◽  
Emission Signal ◽  
Tool Condition

In this work, an online acoustic emission (AE) monitoring system is developed, to investigate the effect of tool wear during the microturning of titanium alloy with a tungsten carbide insert of nose radius 0.1 mm. The AE signal parameters were analyzed in time domain, frequency domain and discrete wavelet transformation (DWT) techniques to correlate with the tool wear status. The root mean square (AERMS) and specific AE energies are also computed for the decomposed AE signals, using the DWT. The results demonstrated that dominant frequency and DWT techniques are found to be most suitable for online tool condition monitoring, using AE sensors in the microturning of titanium alloy.

Local Region Mahalanobis Distance Feature and Its Application in Monitoring Wearing and Breakage States of Cutting Tools

2004 ◽  
Vol 471-472 ◽  
pp. 418-421
Author(s):  
Shi Ming Ji ◽  
Li Zhang ◽  
Y.H. Wan ◽  
X. Zhang ◽  
Ju Long Yuan ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Mahalanobis Distance ◽  
Cutting Tool ◽  
Cutting Tools ◽  
Tool Condition Monitoring ◽  
Local Region ◽  
Structure Form ◽  
Tool Condition ◽  
On Line ◽  
New Research

The Mahalanobis distance feature proposed by P.C .Mahalanobis, an indian statistician. In this paper, we propose a new concept, Local Region Mahalanobis Distance feature –LRMD feature, we shall discuss the structure form, the obtaining methods of LRMD feature from an image and the relations between the LRMD feature and wearing and breakage states of cutting tools. The new research results indicate that the method of automatic on-line cutting tool condition monitoring based on LRMD feature can has better inspect result than the method of Mahalanobis Distance feature.

Intelligent Tool Condition Monitoring System Based on Rough Sets and Mathematical Morphology

2007 ◽  
Vol 10-12 ◽  
pp. 722-726 ◽  
Author(s):  
Li Zhang ◽  
Shi Ming Ji ◽  
Yi Xie ◽  
Qiao Ling Yuan ◽  
Yin Dong Zhang ◽  
...  
Keyword(s):  
Tool Wear ◽  
Mathematical Morphology ◽  
Condition Monitoring ◽  
Rough Sets ◽  
Cutting Tool ◽  
Cutting Tools ◽  
Tool Condition Monitoring ◽  
Online Tool ◽  
Tool Condition ◽  
Condition Monitoring System

The image of cutting tools provides reliable information regarding the extent of tool wear. In this paper, we propose the theory of image processing based on rough sets and mathematical morphology to analyzing the flank faces which are chosen as our monitoring object. First, through plotting the appropriate subset, the rough sets filter is used to enhancement the image of tool wear. Then, the mathematical morphology theory is applied to process the translated binary image. Finally, tool condition monitoring is realized by measuring the area of tool wear. This paper gives the corresponding monitoring principal and proposes a new algorithm to process the cutting tool image. The algorithm is also flexible and fast enough to be implemented in real time for online tool wear or tool condition monitoring.

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