Machine learning based tool condition classification using acoustic emission and vibration data in high speed milling process using wavelet features

Krishnakumar, P.; Rameshkumar, K.; Ramachandran, K. I.

doi:10.3233/idt-180332

Cited by 41 publications

(14 citation statements)

References 44 publications

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“…The lower value of MSE provides the better performance. prediction accuracy can be enhanced by applying machine learning algorithms (Krishnakumar et al, 2018a, Krishnakumar et al, 2018c, Krishnakumar et al, 2018b.…”

Section: Figure 4 Resultant Vibration Signatures For Different Toolsmentioning

confidence: 99%

Decision support system for tool condition monitoring in milling process using artificial neural network

Mohanraj

Tamilvanan²

2021

JER is an international, peer-reviewed journal that publishes f

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This work discusses the development of tool condition monitoring system (TCMs) during milling of AISI stainless steel 304 using sound pressure and vibration signals. Response Surface Methodology (RSM) was used to design the experiments. The various milling parameters and vegetable-based cutting fluids (VBCFs) were optimized to reduce the surface roughness and flank wear. The experimental results reveal the direct relationship between the flank wear and sound and vibration signals. The various statistical parameters were extracted from the measured signals and given as input data to train the artificial neural network (ANN). From the developed ANN model, the flank wear was predicted with the mean squared error (MSE) of 0.0656 mm.

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Section: Figure 4 Resultant Vibration Signatures For Different Toolsmentioning

confidence: 99%

Decision support system for tool condition monitoring in milling process using artificial neural network

Mohanraj

Tamilvanan²

2021

JER is an international, peer-reviewed journal that publishes f

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“…Zhang et al, (2016) proposed a neuro-fuzzy model to predict the tool wear. Krishnakumar et al (2018) proposed a wavelet based tool condition classification using vibration and AE data. Machine learning classifiers are used in their study to predict the tool conditions.…”

Section: Machine Learning Algorithms For Tool Condition Classificationmentioning

confidence: 99%

Feature level fusion of vibration and acoustic emission signals in tool condition monitoring using machine learning classifiers

Kumar

Ramachandran

2020

IJPHM

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To implement the tool condition monitoring system in a metal cutting process, it is necessary to have sensors which will be able to detect the tool conditions to initiate remedial action. There are different signals for monitoring the cutting process which may require different sensors and signal processing techniques. Each of these signals is capable of providing information about the process at different reliability level. To arrive a good, reliable and robust decision, it is necessary to integrate the features of the different signals captured by the sensors. In this paper, an attempt is made to fuse the features of acoustic emission and vibration signals captured in a precision high speed machining center for monitoring the tool conditions. Tool conditions are classified using machine learning classifiers. The classification efficiency of machine learning algorithms are studied in time-domain, frequencydomain and time-frequency domain by feature level fusion of features extracted from vibration and acoustic emission signature.

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“…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]. In a surface grinding process, dresser conditions of aluminium oxide (Al 2 O 3 ) grinding wheel was studied by Alexandre et al [3] with AE and fuzzy methods.…”

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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Machine learning based tool condition classification using acoustic emission and vibration data in high speed milling process using wavelet features

Cited by 41 publications

References 44 publications

Decision support system for tool condition monitoring in milling process using artificial neural network

Decision support system for tool condition monitoring in milling process using artificial neural network

Feature level fusion of vibration and acoustic emission signals in tool condition monitoring using machine learning classifiers

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

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