Optimization of wood machining parameters using artificial neural network in CNC router

Abstract

This study aims to determine the optimal CNC (Computer Numerical Control) machining conditions using an artificial neural network. For this purpose, Fagus orientalis, Castanea sativa, Pinus sylvestris, and Picea orientalis wood samples at 8%, 12%, and 15% moisture content (MC) were machined on a CNC router in both across and along the grain directions. Based on the experimental data of surface roughness and cutting power analyses, a total of 16 models were used. These were selected in hundreds of models that have the lowest error. The spindle speed, feed rate, and the number of cutter teeth were chosen to be different with the literature based on the length of cutter mark. As a result, optimum machining parameters were determined for each wood MC.

KEYWORDS:

• Artificial neural network
• optimal machining conditions
• wood surface roughness
• wood cutting power

Acknowledgments

The authors would like to thank the financial funding of this study supported by The Scientific and Technological Research Council of Turkey (TUBITAK) (Project No: 118O556).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by T?rkiye Bilimsel ve Teknolojik Arastirma Kurumu [Grant Number 118O556].