Tool Wear Condition Prediction Using Vibration Signals in High Speed Machining (HSM) of Titanium (Ti-6Al-4V) Alloy

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

doi:10.1016/j.procs.2015.04.049

Cited by 69 publications

(26 citation statements)

References 8 publications

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“…Wang et al (2011) proposed the tool condition prediction model using SVM. Decision tree and ANN were used for end mill tool condition monitoring by Krishnakumar et al (2015). Zhang et al, (2016) proposed a neuro-fuzzy model to predict the tool wear.…”

Section: Machine Learning Algorithms For Tool Condition Classificationmentioning

confidence: 99%

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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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Section: Machine Learning Algorithms For Tool Condition Classificationmentioning

confidence: 99%

“…Lamraoui et al (2014) studied chatter and its influence in a milling process. Tool conditions in a high speed precision milling machine was studied by Krishnakumar et al (2015) using vibration signals. Tool condition classification efficiency of various machine learning algorithms was studied by them.…”

Section: Introductionmentioning

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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“…More advanced methods with a step closer to automation have been developed to improve the performance of tool condition monitoring. For instance, vibration combined with ML have been reported in [15,16]. These works focus on milling where acceleration signals are collected, filtered and more than ten statistical features extracted and identified.…”

Section: Tool Wear Monitoringmentioning

confidence: 99%

Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach

Terrazas

Martínez-Arellano

Benardos

et al. 2018

JMMP

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The new generation of ICT solutions applied to the monitoring, adaptation, simulation and optimisation of factories are key enabling technologies for a new level of manufacturing capability and adaptability in the context of Industry 4.0. Given the advances in sensor technologies, factories, as well as machine tools can now be sensorised, and the vast amount of data generated can be exploited by intelligent information processing techniques such as machine learning. This paper presents an online tool wear classification system built in terms of a monitoring infrastructure, dedicated to perform dry milling on steel while capturing force signals, and a computing architecture, assembled for the assessment of the flank wear based on deep learning. In particular, this approach demonstrates that a big data analytics method for classification applied to large volumes of continuously-acquired force signals generated at high speed during milling responds sufficiently well when used as an indicator of the different stages of tool wear. This research presents the design, development and deployment of the system components and an overall evaluation that involves machining experiments, data collection, training and validation, which, as a whole, has shown an accuracy of 78%.

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“…Machine learning techniques are compared with deep learning techniques in order find out better one. Decision Tree is a simplest method used to find classification efficiency and having a tree like structure [5]. In SVM a set of inputs with output values is given to learning machine and it builds line between two different sets of data which separates the sets of data.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Gearbox using Machine Learning and Deep Learning Techniques

2019

IJEAT

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Gearbox is an important component used for automobiles, machine tools, industries etc. Failure of any component in gearbox will cause huge maintenance cost and production loss. Failure should be detected as early as possible in order to avoid sudden breakdown which even cause catastrophic failures. Vibration signals are used for machine condition monitoring for predictive maintenance and efficiently predicts fault in the gearbox. In this paper signals from vibration is used for diagnosis of gearbox fault. The experiment uses four different conditions of gearbox in four different load conditions. Then statistical feature extraction is done and obtained result is given to Decision Tree, Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) for fault diagnosis. The efficiency of these four techniques is compared and shows that machine learning is better than deep learning in gearbox fault diagnosis.

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Tool Wear Condition Prediction Using Vibration Signals in High Speed Machining (HSM) of Titanium (Ti-6Al-4V) Alloy

Cited by 69 publications

References 8 publications

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

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

Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach

Fault Diagnosis of Gearbox using Machine Learning and Deep Learning Techniques

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