Tool wear and surface quality assessment of CFRP trimming using fractal analyses of the cutting force signals

Rimpault, Xavier; Châtelain, Jean-François; Klemberg-Sapieha, J.E.; Balazinski, Marek

doi:10.1016/j.cirpj.2016.06.003

Cited by 43 publications

(20 citation statements)

References 17 publications

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“…Few of those fractal analysis have been studied to analyze cutting force signals acquired while machining composite plates [11]. One of them, the regularization fractal analysis, produced the best results in terms of calculation time and performance to evaluate the tool wear.…”

Section: Fractal Analysis Methodsmentioning

confidence: 99%

“…Rimpault et al applied the regularization fractal analysis for cutting force and acoustic emission signals in composite trimming, drilling and milling as well as multimaterial stack drilling [11,[14][15][16].…”

Section: Application To the Machining Process Monitoringmentioning

confidence: 99%

“…As previously stated, the most well-known fractal analysis technique is the box-counting method. Nevertheless, this method has numerous drawbacks and the reader is advised to favor more robust techniques such as regularization [11].…”

Section: Suggestions For Fractal Analysis Implementationmentioning

confidence: 99%

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Fractal Analysis Application Outlook for Improving Process Monitoring and Machine Maintenance in Manufacturing 4.0

Rimpault

Balazinski

Châtelain

2018

JMMP

Self Cite

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Industry 4.0 has been advertised for a decade as the next disruptive evolution for production. It relies on automation growth and particularly on data exchange using numerous sensors in order to develop faster production with tight monitoring. The huge amount of data generated by clouds of sensors during production is often used to feed machine learning systems in order to detect faults, monitor and find possible ways for improvement. However, the artificial intelligence within machine learning requires finding and selecting key features, such as average and root mean square. While current machine learning has already proven its use in diverse applications, its efficiency could be further improved by generating better characteristics such as fractal parameters. In this paper, fractal analysis concept is presented and its current and future applications in machining are discussed. This sensitive and robust technique is already extracting high performance key features that could fill in monitoring and prediction systems. On top of improving features selection and, thus, improving the overall performance of monitoring and predictive systems in machining, this could lead to a more rapid artificial intelligence implementation into manufacturing.

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Section: Fractal Analysis Methodsmentioning

confidence: 99%

Section: Application To the Machining Process Monitoringmentioning

confidence: 99%

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Fractal Analysis Application Outlook for Improving Process Monitoring and Machine Maintenance in Manufacturing 4.0

Rimpault

Balazinski

Châtelain

2018

JMMP

Self Cite

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“…7. Time domain diagram of signal after WPD [3,1] 1250 to 2500 2 9 =512 3 [3,2] 2500 to 3750 2 9 =512 4 [3,3] 3750 to 5000 2 8 =256 5 [3,4] 5000 to 6250 2 8 =256 6 [3,5] 6250 to 7500 2 7 =128 7 [3,6] 7500 to 8750 2 7 =128 8 [3,7] 8750 to 10000 2 7 =128…”

Section: Comparison Of Classical Spectrum Subtraction Wavelet Packetmentioning

confidence: 99%

Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

Zhou¹,

Li²,

Liu³

2019

SV-JME

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To address the difficulty in extracting the features of vibration signals under intense background noise, a new method is proposed based on wavelet packet multi-band spectral subtraction to intensify vibration signal features in drilling processing. First, it is assumed that the spindle vibration signal of machine idling and the vibration signal caused when the tool cuts the workpiece are independent of each other, and the machine's idling signal is perceived as the 'additive noise' of the monitoring signal in light of the spectral subtraction principles. Secondly, in line with the characteristics of vibration signals in the drilling process, the 'additive noise' and monitoring signal are split into multiple frequency bands via wavelet packet decomposition. Eventually, spectral subtraction is performed independently in each band, and the vibration signals are reconstructed. The simulations and experimental results indicate that the new method should effectively eliminate the impact of environmental noise on the process of feature extraction to intensify the features of the monitoring signal. Highlights• A new method is proposed based on wavelet packet multi-band spectral subtraction to intensify vibration signal features in drilling processing. • We have addressed the difficulty in extracting the weak features of vibration signals under intense background noise. • Compared with the traditional threshold de-noising method, the problem of threshold selection can be avoided.

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“…In [6], force-based tool-condition monitoring for a turning process using support vector regression analysis is used to establish the flank wear of the cutting tool. Cutting force signals are also used in [7] to estimate the tool wear and the surface quality, and in [8], a partial least-square regression method is presented to make the tool-wear prediction also on the basis of the force signal. Related to cutting-force measurements are measurements of the current amplitude of the main drive of the machine tool, presented in [9,10], where it is shown that this parameter can also be linked to tool-wear development.…”

Section: Introductionmentioning

confidence: 99%

Tool-Wear Analysis Using Image Processing of the Tool Flank

et al. 2017

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Flexibility of manufacturing systems is an essential factor in maintaining the competitiveness of industrial production. Flexibility can be defined in several ways and according to several factors, but in order to obtain adequate results in implementing a flexible manufacturing system able to compete on the market, a high level of autonomy (free of human intervention) of the manufacturing system must be achieved. There are many factors that can disturb the production process and reduce the autonomy of the system, because of the need of human intervention to overcome these disturbances. One of these factors is tool wear. The aim of this paper is to present an experimental study on the possibility to determine the state of tool wear in a flexible manufacturing cell environment, using image acquisition and processing methods.

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Tool wear and surface quality assessment of CFRP trimming using fractal analyses of the cutting force signals

Cited by 43 publications

References 17 publications

Fractal Analysis Application Outlook for Improving Process Monitoring and Machine Maintenance in Manufacturing 4.0

Fractal Analysis Application Outlook for Improving Process Monitoring and Machine Maintenance in Manufacturing 4.0

Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

Tool-Wear Analysis Using Image Processing of the Tool Flank

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