Face milling tool condition monitoring using sound signal

Madhusudana, C K; Kumar, Hemantha; Narendranath, S.

doi:10.1007/s13198-017-0637-1

Cited by 34 publications

(23 citation statements)

References 33 publications

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“…[104] use the SVM technique to classify milling tool conditions. Discrete wavelength transform extracts the feature from sound sensor signals and found that SVM is an efficient classifier compare to other classifiers use in face milling operation [104]. According to [126], the nonlinear feature reduction and SVM estimate the tool wear and calculate the RUL of the tool [126].…”

Section: A Support Vector Machine (Svm)mentioning

confidence: 99%

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

et al. 2021

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An increase in unplanned downtime of machines disrupts and degrades the industrial business, which results in substantial credibility damage and monetary loss. The cutting tool is a critical asset of the milling machine; the failure of the cutting tool causes a loss in industrial productivity due to unplanned downtime. In such cases, a proper predictive maintenance strategy by real-time health monitoring of cutting tools becomes essential. Accurately predicting the useful life of equipment plays a vital role in the predictive maintenance arena of industry 4.0. Many active research efforts have been done to estimate tool life in varied directions. However, the consolidated study of the implemented techniques and future pathways is still missing. So, the purpose of this paper is to provide a systematic and comprehensive literature survey on the data-driven approach of Remaining Useful Life (RUL) estimation of cutting tools during the milling process. The authors have summarized different monitoring techniques, feature extraction methods, decision-making models, and available sensors currently used in the data-driven model. The authors have also presented publicly available datasets related to milling under various operating conditions to compare the accuracy of the prediction model for tool wear estimation. Finally, the article concluded with the challenges, limitations, recent advancements in RUL prognostics techniques using Artificial Intelligence (AI), and future research scope to explore more in this area.

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Section: A Support Vector Machine (Svm)mentioning

confidence: 99%

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

et al. 2021

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“…Several time-domain features extracted and the most well related features with the tool wear were selected according to correlation coefficients. Several research works which performed in TCM trend, used optical microscope in the offline system [23][24] [25][3] [26]. In our study, we used the new developed vision system to inspect of tool wear in the online system.…”

Section: Experimental Work Setupmentioning

confidence: 99%

Tool Wear Inspection and Classiﬁcation by Artiﬁcial Neural Network Based on Vision System and Cutting Force Signal.

Abdeltawab

Zhang

et al. 2021

Preprint

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The current work focuses on the cutting tool condition monitoring of end milling based on direct and indirect approach in machining AISI H13 alloy steel. Indirect process parameters such as cutting force signals are measured as responses using force sensor. In order to successfully inspect the milling tool life online for direct approaches, an automated machine vision system was used for tool condition monitoring. The image processing algorithms are developed to extract different features of rotating milling tool. A detection and compensation system for tool wear based on machine vision is designed. Feedforward Back-Propagation Neural Network applied for tool wear classification developed based on many force features. Ten time-domain features extracted and the sensitive features is determined based on Pearson’s correlation coefficient. I-kaz method which integrates between kurtosis and standard deviation is added as input feature with the ten time-domain features. A strong correlation is established between most of time-domain features and tool wear with high correlation coefficient. ANN model applied for classification tool states as normal and abnormal. Experiments with vision system have shown that area of wear at bottom and ﬂank is suitable to inspect in-process. Actual measurements of the tool wear stages are possible to identify the abnormality in cutting using vision system. ANN model showed superior results for tool states classification. The mean squared Error (MSE) for classification model was less than 6.7E-09 and R equal to 1. The model can be used to construct fault estimation mode for tool wear online classification and inspection.

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“…The tool condition was estimated with the Artificial Intelligence (AI) techniques based on the acquired signals. The decision tree and discrete wavelet transform (DWT) techniques with sound signals were proposed by Madhusudana et al [38,39] for fault diagnosis of the face milling tool. Close correlation between the acoustic signal and the cutting forces, material removal rate, and tool deflection as well as surface error was reported in [40] for free form milling with the use of a ball end milling cutter.…”

Section: Review Of Related Research Workmentioning

confidence: 99%

A pilot study to assess manufacturing processes using selected point measures of vibroacoustic signals generated on a multitasking machine

Deja

Licow

2020

Int J Adv Manuf Technol

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The article presents the method for the evaluation of selected manufacturing processes using the analysis of vibration and sound signals. This method is based on the use of sensors installed outside the machining zone, allowing to be used quickly and reliably in real production conditions. The article contains a developed measurement methodology based on the specific location of microphones and vibration transducers mounted on the tested object, in this case on a four-axis CNC ST20Y Haas lathe. A mobile phone was integrated into the measuring system and used to control the measurement process. The results from the analysis of vibration and sound signals recorded during different machining operations are presented. They refer to selected working conditions of a machine tool depending on switching the coolant supply on or off and different machine loads caused by various technological processing as well as the various speed of the positioning movements. The analysis was carried out using selected point measures describing the vibroacoustic signals. The synthesis conducted on the basis of results from the experiments indicates the validity of using vibration and acoustic signals, recorded outside the machining area, to evaluate material removal processes that are diverse in terms of kinematics and processing conditions. It indicates the possibility of using proposed point measures of vibroacoustic signals in the diagnostic aspects of the machine tools to achieve high dimension and shape accuracy and to evaluate the condition of the technological devices in terms of their optimal efficiency. Presented methodology can be used as a supporting tool in the CAD/CAM software for a better selection of appropriate cutting parameters and for a wireless control of manufacturing systems consisting of several machine tools.

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Face milling tool condition monitoring using sound signal

Cited by 34 publications

References 33 publications

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

Tool Wear Inspection and Classiﬁcation by Artiﬁcial Neural Network Based on Vision System and Cutting Force Signal.

A pilot study to assess manufacturing processes using selected point measures of vibroacoustic signals generated on a multitasking machine

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