Tool Condition Monitoring Of Cylindrical Grinding Process Using Acoustic Emission Sensor

Aravind, Arun; Rameshkumar, K.; Unnikrishnan, D.; Sumesh, A.

doi:10.1016/j.matpr.2018.02.162

Cited by 43 publications

(13 citation statements)

References 26 publications

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“…Dias et al [11] identified a range of frequency for the occurrence of roundness, cylindricity and surface roughness. In a cylindrical grinding process, Arun et al [12] predicted grinding wheel conditions in a cylindrical grinding process using classifiers such as SVM, decision trees and artificial neural network (ANN). Off late, decision tree, Naive Bayes, SVM and artificial neural network models are used to predict the tool conditions in high-speed precision machining process [13][14][15].…”

Section: Grinding Process Monitoringmentioning

confidence: 99%

Acoustic Emission-Based Grinding Wheel Sharpness Monitoring Using Machine Learning Classifier

Revant

Kumar

Rameshkumar

et al. 2020

Lecture Notes in Mechanical Engineering

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Grinding is one of the important secondary manufacturing processes used to improve the dimensional accuracy, surface finish and geometric form of the component. Grinding wheel consists of abrasive particles, which perform the metal removal function. The sharpness of the grinding wheel is one of the important factors for achieving the required surface geometry in the component. In this study, a simple device used to measure the sharpness of the abrasive particles of the grinding wheel is designed and fabricated. Aluminium oxide grinding wheel conditions are established using the sharpness of the abrasive grinding wheel. Grinding process is monitored using acoustic emission (AE) Sensor. AE features are extracted in time domain and dominated features which contain useful information about the grinding wheel that are identified. A correlation between grinding wheel condition and AE feature is established using ANN-based machine learning classifier.

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Section: Grinding Process Monitoringmentioning

confidence: 99%

Acoustic Emission-Based Grinding Wheel Sharpness Monitoring Using Machine Learning Classifier

Revant

Kumar

Rameshkumar

et al. 2020

Lecture Notes in Mechanical Engineering

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“…A high-frequency acoustic emission signal with further acquired data was used to develop characteristic factors to predict product quality and to detect tool defects [24]. In one study, acoustic signals were captured for an entire grinding cycle until abrasive grains of girding wheel become dull [25]. Various features of the acoustic emission signatures were extracted from the timedomain and correlated with the surface roughness.…”

Section: The Sound Pattern As the Basis For Identification Of Type Anmentioning

confidence: 99%

Use of sound spectral signals analysis to assess the technical condition of mechanical devices

Maciąg

Chałko

2019

MATEC Web Conf.

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The article presents the results of a part of research conducted by the authors into a wide range of work on automation of public safety systems. Their main purpose is to create, on the basis of sound signals analysis, system of warning against the use of firearms monitored public places not only at the time of shooting, but at the stage of preparing - the firearm reloading process. The range of acoustic measurements presented in the article and their analyzes included variability of sound emission of selected type of firearms depending on the number of operations and thus the degree of wear. Analysis of the recorded data will allow in the future to develop a system enabling not only to determine a type of firearm or tp narrow down the search area of individual specimens, but also to determine the degree of wear in terms of suitability for further use. The results discussed in the article correspond to two states of the same firearm model. The first is the initial state from the manufacturer’s factory after returning 50 control shots, the other after over 20,000 cycles. The used firearm was technically fully efficient, with no need to replace the elements during operation as well as tests. The presented scope of measurements includes spectral analysis of sound in the near field and time analysis of sound emission levels in the successive operation phases of the firearm mechanisms.

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“…An online monitoring system for machining processes could have remarkable impacts on a CNC machine tools system in reducing manufacturing cost and time in the product inspections, and avoiding the need for postprocess quality control [1] [2]. Online monitoring techniques allow the real time evaluation of crucial aspects of the machining processes, such as tool condition [3] [4], chatter [5], surface finish [6] [7], chip formation [8], surface damage [9] [10], and so on. In order to provide effective information with online monitoring techniques, the selection of adequate sensors, signal processing methods together with predictive techniques should be optimised according to the specific parameters under analysis.…”

Section: Introductionmentioning

confidence: 99%

“…For online process monitoring, different signal processing methods in time domain and frequency domain have been applied, i.e., time direct analysis (TDA) [6], singular spectrum analysis (SSA) [11], Fourier transform [6], and wavelet transform [12], and so on. Considering correlating features of the parameters under study, several predictive techniques have been applied in many researches, i.e., the multivariate regression [13], the artificial neural networks [6] and the support vector machines (SVM) [4].…”

Section: Introductionmentioning

confidence: 99%

“…The method was based on the detection of the effective width of the grinding wheel with an instrumented diamond tip. Rameshkumar et al [4] registered the acoustic signal in grinding operation to predict the wear level of the grinding wheel. The acoustic emission was analysed in time domain using different features such as root mean square, amplitude, ring-down count, average signal level.…”

Section: Introductionmentioning

confidence: 99%

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Abrasive Feature Related Acoustic Emission in Grinding

Plaza

Chen

Ouarab

2019

2019 25th International Conference on Automation and Computing (ICAC)

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Tool Condition Monitoring Of Cylindrical Grinding Process Using Acoustic Emission Sensor

Cited by 43 publications

References 26 publications

Acoustic Emission-Based Grinding Wheel Sharpness Monitoring Using Machine Learning Classifier

Acoustic Emission-Based Grinding Wheel Sharpness Monitoring Using Machine Learning Classifier

Use of sound spectral signals analysis to assess the technical condition of mechanical devices

Abrasive Feature Related Acoustic Emission in Grinding

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