Modeling workpiece vibrations with neural networks

Tansel, İbrahim N.; Tziranis, Alexander; Wagiman, Amir

doi:10.1007/bf00124983

Cited by 2 publications

(1 citation statement)

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“…13 The dynamic characteristics of a long slender bar were presented with neural networks, but it requires very long training times. 14 The adaptive control system using neural networks and fuzzy logic had been proposed based on the cutting forces to monitor the surface roughness in the ball-end milling. 15,16 The proposed system still cannot be used in practice due to the sudden change in the cutting forces and a large amount of experiences.…”

Section: Introductionmentioning

confidence: 99%

A wavelet approach to predict surface roughness in ball-end milling

Tangjitsitcharoen

Thesniyom

Ratanakuakangwan

2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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This research proposed an advance in the prediction of the in-process surface roughness during the ball-end milling process by utilizing the wavelet transform to monitor and decompose the dynamic cutting forces. The chatter detection system has been adopted from the previous research of the author to avoid the chatter first, and hence, the dynamic cutting force ratio is introduced to predict the in-process surface roughness during the normal cutting by taking the ratio of the decomposed dynamic cutting force in X axis to that in Z axis. The Daubechies wavelet transform is employed in this research to analyze the in-process surface roughness. The experimentally obtained results showed that the surface roughness frequency occurred at the same level of the decomposed dynamic cutting forces although the cutting conditions are changed. It is understood that the in-process surface roughness can be predicted effectively under various cutting conditions referring to the proposed monitoring system.

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