Evaluating the sensitivity of acoustic emission signal features to the variation of cutting parameters in milling aluminum alloys: Part A: frequency domain analysis

2020 ◽  
pp. 095440542094912
Author(s):  
Mohamad Javad Anahid ◽  
Hoda Heydarnia ◽  
Seyed Ali Niknam ◽  
Hedayeh Mehmanparast
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Time Domain ◽  
Acoustic Emission Signal ◽  
Cutting Tool ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Emission Signal ◽  
Signal Parameters ◽  
Signal Features

It is known that adequate knowledge of the sensitivity of acoustic emission signal parameters to various experimental parameters is indispensable. According to the review of the literature, a lack of knowledge was noticeable concerning the behavior of acoustic emission parameters under a broad range of machining parameters. This becomes more visible in milling operations that include sophisticated chip formation morphology and significant interaction effects and directional pressures and forces. To remedy the aforementioned lack of knowledge, the effect of the variation of cutting parameters on the time and frequency features of acoustic emission signals, extracted and computed from the milling operation, needs to be investigated in a wide aspect. The objective of this study is to investigate the effects of cutting parameters including the feed rate, cutting speed, depth of cut, material properties, as well as cutting tool coating/insert nose radius on computed acoustic emission signals featured in the frequency domain. Similar studies on time-domain signal features were already conducted. To conduct appropriate signal processing and feature extraction, a signal segmentation and processing approach is proposed based on dividing the recorded acoustic emission signals into three sections with specific signal durations associated with cutting tool movement within the work part. To define the sensitive acoustic emission parameters to the variation of cutting parameters, advanced signal processing and statistical approaches were used. Despite the time features of acoustic emission signals, frequency domain acoustic emission parameters seem to be insensitive to the variation of cutting parameters. Moreover, cutting factors governing the effectiveness of acoustic emission signal parameters are hinted. Among these, the cutting speed and feed rate seem to have the most noticeable effects on the variation of time–frequency domain acoustic emission signal information, respectively. The outcomes of this work, along with recently completed works in the time domain, can be integrated into advanced classification and artificial intelligence approaches for numerous applications, including real-time machining process monitoring.

Acoustic Emission Signal Classification Using Feature Analysis and Deep Learning Neural Network

2020 ◽  
pp. 2150030
Author(s):  
Jian-Da Wu ◽  
Yu-Han Wong ◽  
Wen-Jun Luo ◽  
Kai-Chao Yao
Keyword(s):  
Acoustic Emission ◽  
Deep Learning ◽  
Frequency Domain ◽  
Acoustic Emission Signal ◽  
Signal Classification ◽  
Learning Method ◽  
Identification Rate ◽  
Emission Signal ◽  
Identification Time ◽  
The Impact

With the development of artificial intelligence in recent years, deep learning has been widely used in mechanical system signal classification but the impact of different feature extractions on the efficiency and effectiveness of deep learning neural networks is more important. In this study, a vehicle classification based on engine acoustic emission signal in the time domain, the frequency domain and the wavelet transform domain for deep learning network techniques is presented and compared. In signal classification, different feature extractions will show in different decomposition levels and can be used to recognize the various acoustic conditions. In the experimental work, as engines from 10 different ground vehicles operate, the measured sound signal is converted into a digital signal, and the established data set is classified and identified by the deep learning method. The number of samples, identification rate and identification time in the various signal domains are compared and discussed in this study. Finally, the experimental results and data analysis show that by using the wavelet signal and the deep learning method, excellent identification time and identification rate can be achieved, compared with traditional time and frequency domain signals.

Development of the technique of clustering by acoustic emission signal parameters

2010 ◽  
Vol 46 (2) ◽  
pp. 137-146 ◽  
Author(s):  
L. N. Stepanova ◽  
K. V. Kanifadin ◽  
I. S. Ramazanov ◽  
S. I. Kabanov
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Emission Signal ◽  
Signal Parameters

Acoustic Emission Signal Acquisition and Analysis on Tool Wear

2014 ◽  
Vol 621 ◽  
pp. 171-178
Author(s):  
Hui Yu Huang ◽  
Yang Hong
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Acoustic Emission Signal ◽  
Cutting Tool ◽  
Signal Acquisition ◽  
Monitoring Methods ◽  
Damage State ◽  
Emission Signal

In the field of machinery manufacture, broken state at the time of the cutting tool in cutting metal, recognition has always been a study is of great significance. Currently, for the state of tool wear and collapse edge damage identification method already has a mature experience. However the existing condition monitoring methods are often used in accuracy and convenience has limitations, this paper USES the acoustic emission technology, as a kind of integrated online test sys tem design lay the foundation. This paper aimed at the sensor in the wireless transmission module, the performance characteristics of tool condition monitoring system of the main structure was designed, and then by acoustic emission signal from the cutting tool in cutting process as the research object, studies the cutting tool characteristics of acoustic emission signal under different damage state, for the on-line monitoring system design and calibration to provide theoretical support.

Experiment Study on Vibration in Milling of Stainless Steel 304 in Time Domain and Frequency Domain

2012 ◽  
Vol 548 ◽  
pp. 406-411
Author(s):  
Yang Shen ◽  
Yong Jie Chen ◽  
Hai Tao Fang ◽  
Jia Pang
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Stainless Steels ◽  
Forced Vibration ◽  
Cutting Tool ◽  
Cutting Speed ◽  
Vibration Acceleration ◽  
Machining Quality ◽  
The Impact ◽  
Acceleration Signals

Vibration is a common phenomenon in cutting process, which is harmful for machining quality and machine tools. This paper focused on the occurrence and characteristics of vibration of the cutting tool and workpiece by changing cutting speed in milling of stainless steels 304. Vibration acceleration signals of both the cutting tool and the workpiece were sampled and analyzed in time domain and frequency domain. Vibration noise and vibration mark were used to judge the occurrence of violent vibration. In the experiments, both self-excited vibration and violent forced vibration were found at different value of cutting speed. Violent forced vibration was easy to be induced owing to interrupted continuous impulsion cutting.The Maximum amplitude of vibration acceleration signals varied with the cutting speed changing. With the cutting speed increased, the probability of violent vibration increased. Bigger amplitude of vibration will not always lead to vibration mark on surface of workpiece, obvious vibration mark only occurred when n=600 r/min and 700 r/min. In order to reduce the impact of violent vibration on machining quality, more attention should also be paid to the static and dynamics characteristics of the cutting tools and workpiece in milling of stainless steels.

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