scholarly journals Type-2 Fuzzy Modeling for Acoustic Emission Signal in Precision Manufacturing

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Qun Ren ◽  
Luc Baron ◽  
Marek Balazinski
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Fuzzy Modeling ◽  
Machining Process ◽  
Precision Machining ◽  
Precision Manufacturing ◽  
Emission Signal ◽  
Tool Condition ◽  
Ae Signal

This paper presents an application of type-2 fuzzy logic on acoustic emission (AE) signal modeling in precision manufacturing. Type-2 fuzzy modeling is used to identify the AE signal in precision machining. It provides a simple way to arrive at a definite conclusion without understanding the exact physics of the machining process. Moreover, the interval set of the output from the type-2 fuzzy approach assesses the information about the uncertainty in the AE signal, which can be of great value for investigation of tool wear conditions. Experiments show that the development of the AE signal uncertainty trend corresponds to that of the tool wear. Information from the AE uncertainty scheme can be used to make decisions or investigate the tool condition so as to enhance the reliability of tool wear.

Identification of the Minimal Thickness of Cutting Layer Based on the Acoustic Emission Signal

2016 ◽  
Vol 686 ◽  
pp. 39-44 ◽  
Author(s):  
Józef Gawlik ◽  
Joanna Krajewska-Śpiewak ◽  
Wojciech Zębala
Keyword(s):  
Signal Strength ◽  
Machining Process ◽  
Precision Machining ◽  
Emission Signal ◽  
Minimal Thickness ◽  
Machining Allowance ◽  
Forming Precision ◽  
Ae Signal ◽  
Cutting Zone

The chip-forming precision machining process plays a significant role in the mechanical technology. In planning of machining operation, it is crucial to supply the information about the possible minimal value of the machining allowance. For the technologist, when planning the machining operation, it is important to define the minimal thickness of cutting layer correctly. This article presents a new method of describing the start of decohesion process in a workpiece, meaning the determination of the minimal thickness of cutting layer based on the AE signal generated in the cutting zone. The research conducted on the turning of an alloy steel and the analysis of the AE signal strength confirmed that the proposed method opens new possibilities in quickening the identification of the minimal thickness of cutting layer under normal machining conditions.

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.

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.

Acoustic Emission-Based Tool Condition Classification in a Precision High-Speed Machining of Titanium Alloy: A Machine Learning Approach

2018 ◽  
Vol 17 (03) ◽  
pp. 1850017 ◽  
Author(s):  
P. Krishnakumar ◽  
K. Rameshkumar ◽  
K. I. Ramachandran
Keyword(s):  
Acoustic Emission ◽  
Titanium Alloy ◽  
Tool Wear ◽  
High Speed ◽  
Machining Process ◽  
Support Vector ◽  
Tool Condition ◽  
Wide Range ◽  
Cutting Zone ◽  
Tungsten Carbide Tool

Mechanical and chemical properties of titanium alloy have led to its wide range of applications in aerospace and biomedical industries. The heat generation and its transfer from the cutting zone are critical in machining of titanium alloys. The process of transferring heat from the primary cutting zone is difficult due to poor thermal conductivity of titanium alloy, and it will lead to rapid tool wear and poor surface finish. An effective tool monitoring system is essential to predict such variations during machining process. In this study, using a high-speed precision mill, experiments are conducted under optimum cutting conditions with an objective of maximizing the life of tungsten carbide tool. Tool wear profile is established and tool conditions are arrived on the basis of the surface roughness. Acoustic emission (AE) signals are captured using an AE sensor during machining of titanium alloy. Statistical features are extracted in time and frequency domain. Features that contain rich information about the tool conditions are selected using J48 decision tree (DT) algorithm. Tool condition classification abilities of DT and support vector machines are studied in time and frequency domains.

The Online Monitoring of Surface Quality Based on Time-Frequency Spectrum Analysis of Acoustic Emission

2011 ◽  
Vol 141 ◽  
pp. 564-568
Author(s):  
Chang Liu ◽  
Guo Feng Wang ◽  
Xu Da Qin ◽  
Lu Zhang
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Surface Quality ◽  
Machining Process ◽  
Work Piece ◽  
Time Frequency ◽  
Quality Of Work ◽  
On Line ◽  
Ae Signal

There is a high requirement on the surface quality of work-pieces made of Ni-based super-alloys due to the important application in aviation and aerospace fields, so it is particularly important to implement the on-line monitoring to the surface quality of work-piece in the machining process. The acoustic emission (AE) signal has the relatively superior signal/noise ratio and sensitivity during the process of nickel alloy. Through the analysis of AE signal’s characteristic which comes from the different condition of tool wear, it is an effective mean to evaluate the tool wear condition and monitor the surface quality of work-piece due to the usage of AE during the machining process. This paper indicate that it is simple and intuitive to achieve the on-line monitoring of surface quality which based on spectrum analysis of AE signal and proposed the method of on-line monitoring of the nickel alloy surface quality under different condition of tool wear based on AE time-frequency spectrum.

Acoustic Emission Signal Analysis for Tool Condition Monitoring in Microendmilling of Aluminium Alloy

2014 ◽  
Vol 984-985 ◽  
pp. 25-30
Author(s):  
Muniyandi Prakash ◽  
P. Ravisankar ◽  
Mani Kanthababu
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Aluminium Alloy ◽  
Condition Monitoring ◽  
Dominant Frequency ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
Ae Energy ◽  
Ae Signal ◽  
Ae Signals

In this study, the effect of tool wear is correlated with acoustic emission (AE) signal during microendmilling of aluminium alloy (AA 1100). The AE signals were acquired using Kistler make AE sensor and the signal features are analyzed in time domain (root mean square (RMS)) and frequency domain (dominant frequency and amplitude). The dominant frequency of the AE signal shows increasing trend with increase in the tool wear, where as AERMSshow uneven trend. The discrete wavelet transformation technique (DWT) has also been carried out by decomposing the required AE signal in different frequency bands. The AERMSand specific AE energy were computed for the decomposed AE signals. From the specific AE energy, it is observed that shearing occurs during microendmilling and also found to be similar that of macro-regieme endmilling. The result demonstrated that the AE signals are potential indicator for tool condition monitoring in microendmilling.

Tool Wear Identification in Turning Titanium Alloy Based on SVM

2014 ◽  
Vol 800-801 ◽  
pp. 446-450 ◽  
Author(s):  
Zhi Rong Liao ◽  
Sheng Ming Li ◽  
Yong Lu ◽  
Dong Gao
Keyword(s):  
Support Vector Machine ◽  
Acoustic Emission ◽  
Titanium Alloy ◽  
Tool Wear ◽  
Cutting Tool ◽  
Support Vector ◽  
Test Set ◽  
Tool Condition ◽  
Good Classification ◽  
Ae Signal

Titanium alloy is difficult cutting materials,the samples of toolwear features are hard to acquire because of short tool life. In terms of the characteristic, Support Vector Machine (SVM) is proposed in this paper to monitor tool condition, the energy ratio of six different frequency bands of acoustic emission (AE) signal are extracted as cutting tool state features , SVM is trained and tested using these features ,Good classification results were achieved by using test set.

Experiment on Surface Roughness and Acoustic Emission Signal in Infeed Period of Cylindrical Plunge Grinding

2013 ◽  
Vol 690-693 ◽  
pp. 2442-2445 ◽  
Author(s):  
Hao Lin Li ◽  
Hao Yang Cao ◽  
Chen Jiang
Keyword(s):  
Surface Roughness ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Feed Rate ◽  
Grinding Process ◽  
Experiment Research ◽  
Plunge Grinding ◽  
Emission Signal ◽  
Ae Signal ◽  
Ae Signals

This work presents an experiment research on Acoustic emission (AE) signal and the surface roughness of cylindrical plunge grinding with the different infeed time. The changed infeed time of grinding process is researched as an important parameter to compare AE signals and surface roughnesses with the different infeed time in the grinding process. The experiment results show the AE signal is increased by the increased feed rate. In the infeed period of the grinding process, the surface roughness is increased at first, and then is decreased.

Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling

2014 ◽  
Vol 255 ◽  
pp. 121-134 ◽  
Author(s):  
Qun Ren ◽  
Marek Balazinski ◽  
Luc Baron ◽  
Krzysztof Jemielniak ◽  
Ruxandra Botez ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
Condition Monitoring System
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