Advance in chatter detection in ball end milling process by utilizing wavelet transform

Tangjitsitcharoen, Somkiat; Saksri, Tanintorn; Ratanakuakangwan, Suthas

doi:10.1007/s10845-013-0805-3

Cited by 86 publications

(25 citation statements)

References 16 publications

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“…If the decomposed level of the wavelet transform is predetermined properly, the in-process surface roughness will be predicted correctly. The preliminary experiments and the previous research 22 showed that the use of five levels of wavelet transform is proper to detect the chip breaking and surface roughness signals occurring at the low-frequency range which is less than 500 Hz. It is understood that the lower frequency signal will appear at the higher level of the wavelet transform.…”

Section: Dynamic Cutting Forces Of Surface Roughness and Continuous Cmentioning

confidence: 87%

Monitoring of chip breaking and surface roughness in computer numerical control turning by utilizing wavelet transform of dynamic cutting forces

Tangjitsitcharoen

Samanmit

2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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The aim of this research is to monitor and classify the broken chip signals from the dynamic cutting forces, in order to predict the surface roughness during the computer numerical control turning process utilizing the Meyer wavelet transform to decompose the dynamic cutting forces. The dynamic cutting forces of the broken chips and the surface roughness can be decomposed into the different levels. The levels of decomposed cutting forces can aid to explain the broken chip formation and the surface roughness profile in both time and frequency domains. The experimentally obtained results showed that the surface roughness frequency occurs at the higher level of decomposed cutting forces, especially at the fifth level, although the cutting conditions are changed. However, the chip breaking frequency appears at the lower level, which depends on the cutting conditions and the chip length. The ratio of the fifth level of decomposed feed forces to that of main forces is proposed to predict the surface roughness during the in-process cutting. It is understood that the broken chip formation can be separated clearly and the surface roughness can be predicted well during the cutting, regardless of the cutting conditions.

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Section: Dynamic Cutting Forces Of Surface Roughness and Continuous Cmentioning

confidence: 87%

Monitoring of chip breaking and surface roughness in computer numerical control turning by utilizing wavelet transform of dynamic cutting forces

Tangjitsitcharoen

Samanmit

2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…It also showed good results in stability control during flat-end milling of thin walled structures, when high-speed machining occurs, as it was described by Séguy et al (2010). Tangjitsitcharoen (2012) proved that on-line chatter detection during ball-end milling may be successfully achieved with wavelet transform. At the same time, the combination of wavelet and Hilbert-Huang transforms can simplify and improve accuracy of chatter identification, as it has been shown in the case of flat-end milling by Cao et al (2013).…”

Section: Introduction Abstractmentioning

confidence: 96%

Low-frequency chatter genesis during inclined surface copy-milling with ball-end mill: Experimental study

Shtehin

Seguy

Wagner

et al. 2018

Machining Science and Technology

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“…Additional sensors also provide useful in-process information, as classically for Tool Condition Monitoring [7] [8][9] [10]. Then, advanced signal processing is performed; such as wavelet transform [11], statistics [12], mechanical model [13], Artificial Intelligence (e.g. neural network [14]) or Machine Learning (e.g.…”

Section: Introductionmentioning

confidence: 99%

Continuous improvement of HSM process by data mining

Godreau

Ritou

Chové³

et al. 2018

J Intell Manuf

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The efficient use of digital manufacturing data is a key leverage point of the factories of the future. Automatic analysis tools are required to provide smart and comprehensible information from large process databases collected on shopfloor machines-tools. In this paper, an original and dedicated approach is proposed for the data mining of HSM (High Speed Machining) flexible productions. It relies on an unsupervised learning (by statistical modelling of machining vibrations) for the classification of machining critical events and their aggregation. Moreover, a contextual clustering is suggested for a better data selection, and a visualization of machining KPI for decision aiding. It results in new leverages for decision making and process improvement; through automatic detection of the main faulty programs, tools or machine conditions. This analysis has been performed over two spindle lifespans (18 months) of industrial HSM production in aeronautics and results are presented, which assess the proposed approach.

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Advance in chatter detection in ball end milling process by utilizing wavelet transform

Cited by 86 publications

References 16 publications

Monitoring of chip breaking and surface roughness in computer numerical control turning by utilizing wavelet transform of dynamic cutting forces

Monitoring of chip breaking and surface roughness in computer numerical control turning by utilizing wavelet transform of dynamic cutting forces

Low-frequency chatter genesis during inclined surface copy-milling with ball-end mill: Experimental study

Continuous improvement of HSM process by data mining

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