scholarly journals A Time–Frequency Acoustic Emission-Based Technique to Assess Workpiece Surface Quality in Ceramic Grinding with PZT Transducer

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3913 ◽  
Author(s):  
Martin A. Aulestia Viera ◽  
Paulo R. Aguiar ◽  
Pedro Oliveira Junior ◽  
Felipe A. Alexandre ◽  
Wenderson N. Lopes ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Surface Quality ◽  
Low Cost ◽  
Digital Signal ◽  
Ceramic Materials ◽  
Fast Response ◽  
Machining Process ◽  
Process Conditions ◽  
Time Frequency ◽  
Ceramic Components

Innovative monitoring systems based on sensor signals have emerged in recent years in view of their potential for diagnosing machining process conditions. In this context, preliminary applications of fast-response and low-cost piezoelectric diaphragms (PZT) have recently emerged in the grinding monitoring field. However, there is a lack of application regarding the grinding of ceramic materials. Thus, this work presents an analysis of the feasibility of using the acoustic emission signals obtained through the PZT diaphragm, together with digital signal processing in the time–frequency domain, in the monitoring of the surface quality of ceramic components during the surface grinding process. For comparative purpose, an acoustic emission (AE) sensor, commonly used in industry, was used as a baseline. The results obtained by the PZT diaphragm were similar to the results obtained using the AE sensor. The time–frequency analysis allowed to identify irregularities throughout the monitored process.

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.

Wavelet-Based Online Evaluation and Classification of Wear State Using Acoustic Emission

2012 ◽  
Author(s):  
Dorra Baccar ◽  
Dirk Söffker
Keyword(s):  
Acoustic Emission ◽  
Low Cost ◽  
Time Frequency ◽  
Online Evaluation ◽  
Cost System ◽  
State Classification ◽  
Online Examination ◽  
Field Programmable ◽  
Short Time

Advanced signal processing approaches such time-frequency analysis are widely used for online evaluation, damage detection, and wear state classification. The idea of this paper is to introduce a new methodology for online examination of wear phenomena in metallic structure by means of acoustic emission (AE), Short-Time Fourier Transform (STFT) and Wavelet Transform (WT). The proposed novel low-cost system is developed for analyzing and monitoring specific signals indicating tribological effects with focus on field programmable gate array (FPGA) implementation of discrete WT (DWT). In addition, experimental results obtained from each approach are given showing the success of the introduced approach.

scholarly journals Undervoltage Identification in Three Phase Induction Motor Using Low-Cost Piezoelectric Sensors and STFT Technique

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 72
Author(s):  
Leonardo Carvalho ◽  
Guilherme Lucas ◽  
Marco Rocha ◽  
Claudio Fraga ◽  
Andre Andreoli
Keyword(s):  
Acoustic Emission ◽  
Induction Motor ◽  
Large Scale ◽  
New Technologies ◽  
Low Cost ◽  
Industrial Applications ◽  
Piezoelectric Sensors ◽  
Time Frequency ◽  
Three Phase ◽  
Ae Signals

Three-phase induction motors (IMs) are electrical machines used on a large scale in industrial applications because they are versatile, robust and low maintenance devices. However, IMs are significantly affected when fed by unbalanced voltages. Prolonged operation under voltage unbalance (VU) conditions degrades performance and shortens machine life by producing imbalances in stator currents that abnormally raise winding temperature. With the development of new technologies and research on non-destructive techniques (NDT) for fault diagnoses in IMs, it is relevant to obtain economically accessible, efficient and reliable sensors capable of acquiring signals that allow the identification of this type of failure. The objective of this study is to evaluate the application of low-cost piezoelectric sensors in the acquisition of acoustic emission (AE) signals and the identification of VU through the analysis of short-term Fourier transform (STFT) spectrograms. The piezoelectric sensor makes NDT feasible, as it is an affordable and inexpensive component. In addition, STFT allows time-frequency analyses of acoustic emission signals. In this NDT, two sensors were coupled on both sides of an induction motor frame. The AE signals obtained during the IM operation were processed and the resulting spectrograms were analyzed to identify the different VU levels. After comparing the AE signals for faulty conditions with the signals for the IM operating at balanced voltages, it was possible to obtain a desired identification that confirmed the successful application of low-cost piezoelectric sensors for VU condition detection in three-phase induction machines.

scholarly journals Non-Invasive Estimation of Machining Parameters during End-Milling Operations Based on Acoustic Emission

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5326
Author(s):  
Andrés Sio-Sever ◽  
Erardo Leal-Muñoz ◽  
Juan Manuel Lopez-Navarro ◽  
Ricardo Alzugaray-Franz ◽  
Antonio Vizan-Idoipe ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
End Milling ◽  
Low Cost ◽  
Milling Process ◽  
Machining Process ◽  
Measuring System ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Machining Processes ◽  
Non Invasive

This work presents a non-invasive and low-cost alternative to traditional methods for measuring the performance of machining processes directly on existing machine tools. A prototype measuring system has been developed based on non-contact microphones, a custom designed signal conditioning board and signal processing techniques that take advantage of the underlying physics of the machining process. Experiments have been conducted to estimate the depth of cut during end-milling process by means of the measurement of the acoustic emission energy generated during operation. Moreover, the predicted values have been compared with well established methods based on cutting forces measured by dynamometers.

scholarly journals Erratum: Aulestia Viera, M., et al. A Time–Frequency Acoustic Emission-Based Technique to Assess Workpiece Surface Quality in Ceramic Grinding with PZT Transducer. Sensors 2019, 19, 3913

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2387
Author(s):  
Martin A. Aulestia Viera ◽  
Paulo R. Aguiar ◽  
Pedro Oliveira Junior ◽  
Felipe A. Alexandre ◽  
Wenderson N. Lopes ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Surface Quality ◽  
Workpiece Surface ◽  
Time Frequency ◽  
Ceramic Grinding

The authors wish to make the following erratum to this paper [...]

scholarly journals Tool Health Monitoring Using Airborne Acoustic Emission and Convolutional Neural Networks: A Deep Learning Approach

2021 ◽  
Vol 11 (6) ◽  
pp. 2734
Author(s):  
Muhammad Arslan ◽  
Khurram Kamal ◽  
Muhammad Fahad Sheikh ◽  
Mahmood Anwar Khan ◽  
Tahir Abdul Hussain Ratlamwala ◽  
...  
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Convolutional Neural Networks ◽  
Health Monitoring ◽  
High Speed ◽  
High Speed Steel ◽  
Machining Process ◽  
Turning Process ◽  
Cnc Machine ◽  
Time Frequency

Tool health monitoring (THM) is in great focus nowadays from the perspective of predictive maintenance. It prevents the increased downtime due to breakdown maintenance, resulting in reduced production cost. The paper provides a novel approach to monitoring the tool health of a computer numeric control (CNC) machine for a turning process using airborne acoustic emission (AE) and convolutional neural networks (CNN). Three different work-pieces of aluminum, mild steel, and Teflon are used in experimentation to classify the health of carbide and high-speed steel (HSS) tools into three categories of new, average (used), and worn-out tool. Acoustic signals from the machining process are used to produce time–frequency spectrograms and then fed to a tri-layered CNN architecture that has been carefully crafted for high accuracies and faster trainings. Different sizes and numbers of convolutional filters, in different combinations, are used for multiple trainings to compare the classification accuracy. A CNN architecture with four filters, each of size 5 × 5, gives best results for all cases with a classification average accuracy of 99.2%. The proposed approach provides promising results for tool health monitoring of a turning process using airborne acoustic emission.

scholarly journals Improvement in Contact Strength of Si3N4/SiC Composite by Crack Healing

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Koji Takahashi ◽  
Keita Takahashi ◽  
Kotoji Ando
Keyword(s):  
Acoustic Emission ◽  
Cutting Tool ◽  
Indentation Test ◽  
Machining Process ◽  
Surface Cracks ◽  
Contact Strength ◽  
Crack Healing ◽  
Machining Processes ◽  
Contact Loading ◽  
Ceramic Components

Ceramics have been used as bearing and cutting tool components, which are subjected to contact loading during their operation. The presence of surface cracks on these components decreases their contact strength. Thus, the reliability of ceramic components can be increased by improving their contact strength through crack healing. In the present study, the effects of crack healing on the contact strength of a silicon carbide-(SiC-) reinforced silicon nitride (Si3N4) composite subjected to various machining processes were investigated. The contact strength of this composite was evaluated using a sphere indentation test in which acoustic emission was used. The results showed that the contact strength of the composite improved when it was subjected to crack healing in combination with rapping; this was true even when the composite had cracks due to a heavy machining process.

scholarly journals Machined Surface Quality Monitoring Using a Wireless Sensory Tool Holder in the Machining Process

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1847 ◽  
Author(s):  
Zhiyuan Lu ◽  
Meiqing Wang ◽  
Wei Dai
Keyword(s):  
Frequency Domain ◽  
Surface Quality ◽  
Critical Role ◽  
Rejection Rate ◽  
Machining Process ◽  
Quality Monitoring ◽  
Machined Surface ◽  
Time Frequency ◽  
Tool Holder ◽  
Deep Forest

The quality of a machined surface plays a critical role in assembly performance, especially for precise matching parts, and therefore it is necessary to develop a surface quality monitoring system in the machining process. In this paper, an indirect surface quality monitoring approach is proposed with a wireless sensory tool holder. First, experimentation is conducted to collect the machining process signals from the tool holder. Then, the time domain, frequency domain and time–frequency domain features are extracted, and the deep forest algorithm is adopted to identify the surface quality, which is evaluated through the surface average parameter. Finally, the results of the experiment and the comparisons with other approaches demonstrate the effectiveness of the proposed method, which could be applied to ensure the surface quality, improve the machining efficiency and reduce the rejection rate of the machining process.

scholarly journals A Contribution to the Monitoring of Ceramic Surface Quality Using a Low-Cost Piezoelectric Transducer in the Grinding Operation

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 16 ◽  
Author(s):  
Martin Aulestia Viera ◽  
Felipe Alexandre ◽  
Wenderson Lopes ◽  
Paulo de Aguiar ◽  
Rosemar Batista da Silva ◽  
...  
Keyword(s):  
Surface Quality ◽  
Monitoring System ◽  
Piezoelectric Transducer ◽  
Low Cost ◽  
Depth Of Cut ◽  
Grinding Process ◽  
Ceramic Surface ◽  
Workpiece Material ◽  
Time Frequency ◽  
Grinding Conditions

The grinding process is usually one of the last stages in the manufacturing process chain since it can provide superior surface finish and closer dimensional tolerances. However, due to peculiarities of the grinding process, a workpiece material is susceptible to many problems, and demands a reliable real-time monitoring system. Some grinding monitoring systems have been proposed by means of sensors. However, the literature is still scarce in terms of employing time–frequency analysis techniques during the grinding of ceramics. Thus, this paper proposes an application of a low-cost piezoelectric transducer (PZT) in the analysis of the surface quality of ceramic workpieces during the grinding process by means of the frequency–time domain technique along with the ratio of power (ROP) parameter. An integrated, high-cost, commonly-used acoustic emission (AE) sensor was employed in order to compare the results with the low-cost PZT transducer. Tests were performed using a surface grinding machine. Three depth of cut values were selected in order to represent slight, moderate, and severe grinding conditions. Signals were collected at 2 MHz. The short-time Fourier transform (STFT) was studied in order to obtain the frequency variations over time. An analysis of the ROP values was performed in order to establish a correlation with the surface roughness. The ROP values are highly desirable for setting a threshold to detect the workpiece surface quality and for implementing it into a monitoring system. The results using the PZT transducer showed a great similarity to those obtained using the AE sensor.

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