In-Process Prediction of Straightness in CNC Turning by Using Wavelet Transform

Sassantiwong, Mumin; Tangjitsitcharoen, Somkiat

doi:10.2991/gmee-15.2015.51

Cited by 4 publications

(2 citation statements)

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“…The developed model focused on five parameters, which are cutting speed, feed rate, depth of cut, nose radius, and cutting force ratio. Sassantiwong and Tangjitsitcharoen [12] applied the Daubechies wavelet transform to decompose the dynamic cutting forces and identify straightness signal from the other noise signals to gain more accuracy for the straightness prediction of turning process with carbon steel (S45C) workpiece. The dynamic cutting forces were decomposed into ten levels and the 8th level of wavelet transformation is recommended to monitor and predict the in-process workpiece straightness without the effects from the chip conditions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Straightness Prediction in CNC Turning Process for Carbon Steel and Aluminum Workpieces Applying Artificial Neural Networks

2022

IJMLC

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An intelligent machine and manufacturing system has a significant role in the near future, especially when the circumstance of manufacturing industries are seriously competitive. New technologies are continuously being developed to serve future manufacturing. CNC turning machine is widely utilized in various advanced manufacturing industries. Straightness is a critical parameter in CNC turning process, which affects the workpiece assembly directly. However, control of straightness of the workpieces during in-process turning is difficult to be measured. Moreover, CNC turning machine cannot be adjusted real-time without stopping the operation. Hence, the aim of this research is to develop the straightness prediction model in the CNC turning process under various cutting conditions for carbon steel and aluminum workpieces in order to improve in-process monitoring and control of straightness. The cutting forces ratio has been adopted to estimate straightness. The Daubechies wavelet transform is utilized to decompose the dynamic cutting forces to remove the noise signals for better prediction. The straightness is calculated by employing the two-layer feed forward neural network, which is trained with the Levenberg-Marquardt back-propagation algorithm. As a result, the in-process straightness could be predicted well with greater accuracy and reliability using the proposed straightness model.

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Section: Introductionmentioning

confidence: 99%

“…2. The feed force is applied for predicting straightness in many previous researches [10] - [12], [16] because it is sensitive to the straightness profile. Moreover, the measuring direction of the straightness profiles is parallel to the axis of the cylindrical workpiece, which is the same direction as the feed rate direction.…”

Section: Introductionmentioning

confidence: 99%