Abstract—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.
Index Terms—Artificial neural networks, cutting force ratio, straightness, wavelet transform.
The authors are with the Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Phayathai Road, Patumwan, Bangkok, 10330, Thailand (e-mail: somkiat.ta@eng.chula.ac.th, warisara.lai@gmail.com).
Cite: S. Tangjitsitcharoen and W. Laiwatthanapaisan, "Straightness Prediction in CNC Turning Process for Carbon Steel and Aluminum Workpieces Applying Artificial Neural Networks," International Journal of Machine Learning and Computing vol. 12, no. 5, pp. 179-184, 2022.
Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).